Space farming: Horticulture systems on spacecraft and outlook to planetary space exploration.

Successful human space exploration requires more products than can be taken as payload. There is a need, therefore, for in-space circular manufacturing. Requirements for this include limited resource inflow, from either Earth or other planets and the generation of minimal waste. The provision of nutritious food is a clear need for human survival on the Moon or Mars and is one of the most complex to solve. Demand in large quantities, constant and reliable provision of food requires the development of specialist agricultural technologies. Here, we first review the history of space farming over the past five decades. This survey assesses the technologies which have been tested under the harsh conditions of space, identifying which modern horticultural components are applicable for in-space plant growth. We then outline which plants have been grown and under what conditions, and speculate upon the types of plants that could be selected to best nourish astronauts. Current systems are focussed on experimentation and exploration, but do not yet provide turn-key solutions for efficient food production within a long-term space exploration scenario. With that take, this review aims to provide a perspective on how an engineered closed circular environmental life-support system (ECCLES) might be constructed. To exemplify the latter, nutrient auto accumulation by biofortification is proposed through the integration of space farming and space mining, which is uncharted on Earth.

Consequences of Groundwater-Model Vertical Discretization in Risk-Based Decision-Making.

One of the first and most important decisions facing practitioners when constructing a numerical groundwater model is vertical discretization. Several factors will influence this decision, such as the conceptual model of the system and hydrostratigraphy, data availability, resulting computational burden, and the purpose of the modeling analysis. Using a coarse vertical discretization is an attractive option for practitioners because it reduces data requirements and model construction efforts, can increase model stability, and can reduce computational demand. However, using a coarse vertical discretization as a form of model simplification is not without consequence; this may give rise to unwanted side-effects such as biases in decision-relevant simulated outputs. Given its foundational role in the modeled representation of the aquifer system, herein we investigate how vertical discretization may affect decision-relevant simulated outputs using a paired complex-simple model analysis. A Bayesian framework and decision analysis approach are adopted. Two case studies are considered, one of a synthetic, linked unsaturated-zone/surface-water/groundwater hydrologic model and one of a real-world linked surface-water/groundwater hydrologic-nitrate transport model. With these models, we analyze decisions related to abstraction-induced changes in ecologically important streamflow characteristics and differences in groundwater and surface-water nitrate concentrations and mass loads following potential land-use change. We show that for some decision-relevant simulated outputs, coarse vertical discretization induces bias in important simulated outputs, and can lead to incorrect resource management action. For others, a coarse vertical discretization has little or no consequence for resource management decision-making.

Transient Recharge Estimability Through Field-Scale Groundwater Model Calibration.

The estimation of recharge through groundwater model calibration is hampered by the nonuniqueness of recharge and aquifer parameter values. It has been shown recently that the estimability of spatially distributed recharge through calibration of steady-state models for practical situations (i.e., real-world, field-scale aquifer settings) is limited by the need for excessive amounts of hydraulic-parameter and groundwater-level data. However, the extent to which temporal recharge variability can be informed through transient model calibration, which involves larger water-level datasets, but requires the additional consideration of storage parameters, is presently unknown for practical situations. In this study, time-varying recharge estimates, inferred through calibration of a field-scale highly parameterized groundwater model, are systematically investigated subject to changes in (1) the degree to which hydraulic parameters including hydraulic conductivity (K) and specific yield (Sy ) are constrained, (2) the number of water-level calibration targets, and (3) the temporal resolution (up to monthly time steps) at which recharge is estimated. The analysis involves the use of a synthetic reality (a reference model) based on a groundwater model of Uley South Basin, South Australia. Identifiability statistics are used to evaluate the ability of recharge and hydraulic parameters to be estimated uniquely. Results show that reasonable estimates of monthly recharge (<30% recharge root-mean-squared error) require a considerable amount of transient water-level data, and that the spatial distribution of K is known. Joint estimation of recharge, Sy and K, however, precludes reasonable inference of recharge and hydraulic parameter values. We conclude that the estimation of temporal recharge variability through calibration may be impractical for real-world settings.