Upscaling Subsurface Transport from the Column to the Field: A Focus on the Meso-Scale

<p>Upscaling groundwater transport from the column scale to the field scale is relevant because field tests with various tracers are often too expensive or not permissible, due to public health or environmental concerns.  Therefore, when testing chemical or pathogenic tracers, work is often done using small scale columns in the laboratory and results are extrapolated to the field. Several studies compare tracer transport in small-scale columns to tests in the field, but there is yet to be a study that compares groundwater transport using a meso-scale as well. Within a framework of upscaling, three scales are considered: small laboratory columns (0.1 m scale), a large intact core (1 m scale), and a real-world gravel aquifer (10 m scale).  The small column is filled with gravel material taken from boreholes at the field site, which is close to Vienna, Austria.  The meso-scale consists of an undisturbed gravel column, which was taken from a gravel pit near Neuhofen an der Ybbs, Austria. It was found that scale effects observed may be due to heterogeneity at the macropore scale versus preferential flowpaths at the meso-scale and field scale. Additionally, differences may be observed due to the small columns being repacked with aquifer material and the large column and field site being “undisturbed”.  The meso-scale column allows us to gain insight into the upscaling processes by incorporating an in-between step when comparing groundwater transport at the column to the field scale.</p>

Groundwater and Stream Composition

Changes of groundwater chemistry have long been observed. We review some studies of the earthquake-induced changes of groundwater and streamflow composition. When data are relatively abundant and the hydrogeology is relatively simple, the observed changes may provide valuable insight into earthquake-induced changes of hydrogeological processes. Progress in this aspect, however, has been slow not only because systematic measurements are scare but also because of the distribution of chemical sources and sinks in the crust are often complex and unknown. Most changes are consistent with the model of earthquake-enhanced groundwater transport through basin-wide or local enhanced permeability caused by earthquake-induced breaching of hydrologic barriers such as aquitards, connecting otherwise isolated aquifers or other fluid sources, leading to fluid source switching and/or mixing. Because the interpretation of earthquake-induced groundwater and stream compositions is often under-constrained, multi-disciplinary approaches may be needed to provide a better constrained interpretation of the observed changes.

Making Steppingstones out of Stumbling Blocks: A Bayesian Model Evidence Estimator with Application to Groundwater Transport Model Selection

Bayesian model evidence (BME) is a measure of the average fit of a model to observation data given all the parameter values that the model can assume. By accounting for the trade-off between goodness-of-fit and model complexity, BME is used for model selection and model averaging purposes. For strict Bayesian computation, the theoretically unbiased Monte Carlo based numerical estimators are preferred over semi-analytical solutions. This study examines five BME numerical estimators and asks how accurate estimation of the BME is important for penalizing model complexity. The limiting cases for numerical BME estimators are the prior sampling arithmetic mean estimator (AM) and the posterior sampling harmonic mean (HM) estimator, which are straightforward to implement, yet they result in underestimation and overestimation, respectively. We also consider the path sampling methods of thermodynamic integration (TI) and steppingstone sampling (SS) that sample multiple intermediate distributions that link the prior and the posterior. Although TI and SS are theoretically unbiased estimators, they could have a bias in practice arising from numerical implementation. For example, sampling errors of some intermediate distributions can introduce bias. We propose a variant of SS, namely the multiple one-steppingstone sampling (MOSS) that is less sensitive to sampling errors. We evaluate these five estimators using a groundwater transport model selection problem. SS and MOSS give the least biased BME estimation at an efficient computational cost. If the estimated BME has a bias that covariates with the true BME, this would not be a problem because we are interested in BME ratios and not their absolute values. On the contrary, the results show that BME estimation bias can be a function of model complexity. Thus, biased BME estimation results in inaccurate penalization of more complex models, which changes the model ranking. This was less observed with SS and MOSS as with the three other methods.

Closing the N-Budget: How Simulated Groundwater-Borne Nitrate Supply Affects Plant Growth and Greenhouse Gas Emissions on Temperate Grassland

European groundwater reservoirs are frequently subject to reactive nitrogen pollution (Nr) owing to the intensive use of nitrogen (N) fertilizer and animal manure in agriculture. Besides its risk on human health, groundwater Nr loading also affects the carbon (C) and N cycle of associated ecosystems. For a temperate grassland in Germany, the long-term (12 years) annual average exports of Nr in form of harvest exceeded Nr inputs via fertilization and deposition by more than 50 kgN ha−1. We hypothesize that the resulting deficit in the N budget of the plant-soil system could be closed by Nr input via the groundwater. To test this hypothesis, the ecosystem model LandscapeDNDC was used to simulate the C and N cycle of the respective grassland under different model setups, i.e., with and without additional Nr inputs via groundwater transport. Simulated plant nitrate uptake compensated the measured N deficit for 2 of 3 plots and lead to substantial improvements regarding the match between simulated and observed plant biomass and CO2 emission. This suggests that the C and N cycle of the investigated grassland were influenced by Nr inputs via groundwater transport. We also found that inputs of nitrate-rich groundwater increased the modelled nitrous oxide (N2O) emissions, while soil water content was not affected.