• Energy Research

Due to poor water resources management, groundwater-dependent agriculture induces substantial stress on several aquifer systems worldwide, which poses a serious threat to water and food security. However, only a few studies have addressed this vital issue. This study aimed to evaluate stress on aquifers due to the overuse of groundwater for food production and explore pathways for stress reduction via improved irrigation efficiency and productivity. Groundwater stress was characterized using the ratio of water use to availability, with consideration for environmental flows. The results indicated that out of 107 countries—dependent on groundwater irrigation, about half are overexploiting groundwater, while one-fifth of these countries are extracting moderately-to heavily. Over 90% of the non-renewable groundwater abstraction occurs in 7 countries. Further, about 450 million tonnes (Mt) of global annual food production is from non-renewable groundwater exploitation. If the existing irrigation efficiency is increased to 90%, current groundwater stress would be reduced by 40%. Additionally, in unstressed regions, it would be possible to produce additional 300 Mt of food by using saved water while maintaining groundwater stress at acceptable levels. Moreover, improved water productivity in conjunction with increased irrigation efficiency could reduce the current level of unsustainable food production by 47%. These results provide important insights into the dynamics of irrigation stress on groundwater systems, and the role of managerial interventions.

AbstractRainfall‐runoff models are often deficient under changing climatic conditions, yet almost no recent studies propose new or improved model structures, instead focusing on model intercomparison, input sensitivity, and/or quantification of uncertainty. This paucity of progress in model development is (in part) due to the difficulty of distinguishing which cases of model failure are truly caused by structural inadequacy. Here we propose a new framework to diagnose the salient cause of poor model performance in changing climate conditions, be it structural inadequacy, poor parameterization, or data errors. The framework can be applied to a single catchment, although larger samples of catchments are helpful to generalize and/or cross‐check results. To generate a diagnosis, multiple historic periods with contrasting climate are defined, and the limits of model robustness and flexibility are explored over each period separately and for all periods together. Numerous data‐based checks also supplement the results. Using a case study catchment from Australia, improved inference of structural failure and clearer evaluation of model structural improvements are demonstrated. This framework enables future studies to (i) identify cases where poor simulations are due to poor calibration methods or data errors, remediating these cases without recourse to structural changes; and (ii) use the remaining cases to gain greater clarity into what structural changes are needed to improve model performance in changing climate.