Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy

Bayesian inference of microbial soil respiration models is often based on the assumptions that the residuals are independent (i.e., no temporal or spatial correlation), identically distributed (i.e., Gaussian noise), and have constant variance (i.e., homoscedastic). In the presence of model discrepancy, as no model is perfect, this study shows that these assumptions are generally invalid in soil respiration modeling such that residuals have high temporal correlation, an increasing variance with increasing magnitude of CO2 efflux, and non-Gaussian distribution. Relaxing these three assumptions stepwise results in eight data models. Data models are the basis of formulating likelihood functions of Bayesian inference. This study presents a systematic and comprehensive investigation of the impacts of data model selection on Bayesian inference and predictive performance. We use three mechanistic soil respiration models with different levels of model fidelity (i.e., model discrepancy) with respect to the number of carbon pools and the explicit representations of soil moisture controls on carbon degradation; therefore, we have different levels of model complexity with respect to the number of model parameters. The study shows that data models have substantial impacts on Bayesian inference and predictive performance of the soil respiration models such that the following points are true: (i) the level of complexity of the best model is generally justified by the cross-validation results for different data models; (ii) not accounting for heteroscedasticity and autocorrelation might not necessarily result in biased parameter estimates or predictions, but will definitely underestimate uncertainty; (iii) using a non-Gaussian data model improves the parameter estimates and the predictive performance; and (iv) accounting for autocorrelation only or joint inversion of correlation and heteroscedasticity can be problematic and requires special treatment. Although the conclusions of this study are empirical, the analysis may provide insights for selecting appropriate data models for soil respiration modeling.U.S. Department of Energy [DE-SC0019438, DE-SC0008272]; U.S. National Science Foundation [EAR-1552329, OIA-1557349]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Experimental and Geochemical Modeling Evidences of Mineral Sequestration of CO2 in Saline Siliciclastic aquifers

The validity of mineral sequestration in saline siliciclastic aquifers in sedimentary basins is assessed in this paper. Mineral sequestration is the precipitation of carbonates due to the dissolution of silicates upon the injection of CO2 in deep geological formations, while solubility trapping is the dissolution of CO2 in the formation water. Saline reservoirs in sedimentary basins seem to constitute one of the best targets for the storage due to their huge storage capacity, low importance in terms of natural resources and wide availability and in close proximity to power generation plants. Siliciclastic aquifers are predicted to have the best potential for trapping CO2, by precipitating carbonate minerals, when they contain an assemblage of basic aluminosilicate minerals such as fledspars, zeolites, illites, chlorites and smectites. Precipitation of carbonate minerals due to the dissolution of silicate are generally not observed in laboratory experiments conducted at low temperature and pressure due to the slow dissolution rates of silicates, or the absence of significant amount of divalent cations in the rock composition. However, carbonate precipitation is observed in work conducted under relatively high pressure and temperature. On the other hand, although carbonate perception is predicted by computer simulation for larger timeframes as reported in several studies, yet laboratory and geochemical modeling work suggests that the injection of supercritical CO2 in deep saline aquifers may show limited reactivity with reservoir rocks. Thus, the dominant trapping mechanisms will be more based on the dissolution of CO2 in the formation water rather than on mineral sequestration. Accordingly, this article concludes that apart from the physical conditions such as temperature and pressure, mineral sequestration in saline siliciclastic aquifers of sedimentary basins may behave differently due to differences in brine compositions and rock types, and thus the degree of mineral sequestration is case specific

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

Sustainability of Groundwater

Groundwater fills and flows in the pore-space, fractures, and conduits of geological formations beneath the Earth's surface, called aquifers. Groundwater is the Earth's largest non-frozen freshwater reservoir, accounting for more than 97% of the liquid freshwater stock. Groundwater is the world's most extracted natural resource with withdrawal rates in the range of 1000 km3/year, and about 70% of the pumped groundwater is used for agriculture worldwide. Groundwater is a reliable freshwater resource that moves slowly in the aquifer, providing vital benefits for billions of people worldwide. Groundwater supplies more than half of the drinking water; helps to grow food by supplying approximately 40% of irrigation water; accounts for about one third of freshwater supply for industrial activities; and supports groundwater dependent ecosystems in aquifers, soil, rivers, lakes, wetlands, coastal zones, and marine environments, providing numerous ecosystem services. In addition, groundwater is a geothermal energy resource, which can be used for heating and cooling in urban heat islands as an example. Moreover, groundwater can generally serve as a manageable buffer to droughts, surface water seasonal variations, and floods. However, there are growing concerns over unsustainable groundwater pumping exceeding natural and induced recharge along with groundwater contamination and salinization, and degradation of groundwater dependent ecosystems. For example, more than half of the largest aquifers on Earth are being depleted, given estimates derived from the GRACE satellite mission (Richey et al., 2015). In addition, sustainable management of groundwater resources is critical for climate adaptation strategies, as climate change and variability drive the aquifer recharge, and can change groundwater use. As the world population is nearing 8 billion, these essential benefits and growing concerns call for an action to ensure groundwater sustainability (Gleeson et al., 2019). This article shows that understanding the coupled water-ecology-human system in a participatory water governance framework is critical for sustainable groundwater management. In addition, the article discusses the sustainability challenges of coastal and karst aquifers as examples

Groundwater sustainability: a review of the interactions between science and policy

Concerns over groundwater depletion and ecosystem degradation have led to the incorporation of the concept of groundwater sustainability as a groundwater policy instrument in several water codes and management directives worldwide. Because sustainable groundwater management is embedded within integrated, co-evolving hydrological, ecological, and socioeconomic systems, implementing such policies remains a challenge for water managers and the scientific community. The problem is further exacerbated when participatory processes are lacking, resulting in a communication gap among water authorities, scientists, and the broader community. This paper provides a systematic review of the concept of groundwater sustainability, and situates this concept within the calls from the hydrologic literature for more participatory and integrated approaches to water security. We discuss the definition of groundwater sustainability from both a policy and scientific perspective, tracing the evolution of this concept from safe yield, to sustainable groundwater management. We focus on the diversity of societal values related to groundwater sustainability, and the typology of the aquifer performance and governance factors. In addition, we systematically review the main components of an effective scientific evaluation of groundwater sustainability policy, which are multi-process modeling, uncertainty analysis, and participation. We conclude that effective groundwater sustainability policy implementation requires an iterative scientific evaluation that (i) engages stakeholders in a participatory process through collaborative modeling and social learning; (ii) provides improved understanding of the coevolving scenarios between surface water-groundwater systems, ecosystems, and human activities; and (iii) acknowledges and addresses uncertainty in our scientific knowledge and the diversity of societal preferences using multi-model uncertainty analysis and adaptive management. Although the development of such a transdisciplinary research approach, which connects policy, science, and practice for groundwater sustainability evaluation, is still in its infancy worldwide, we find that research towards groundwater sustainability is growing at a much faster rate than groundwater research as a whole