Water competition is a key issue in the study of the water-food-energy nexus (WFEN), which can affect water, food, and energy security and can generate notable challenges in water resource management. Since Bayesian network can express parameter uncertainty with a certain probability distribution while reflecting the dependencies of each variable, this study used a Bayesian network to model the WFEN in the Pearl River Region (PRR). The network structure can intuitively represent complex causal relationships, and the form of the probability distribution can effectively reflect the variable uncertainty. The responses of the Bayesian network model under different scenarios were used to analyse the major influencing factors, and water competition relationships in various sectors were explored. The results indicated that water competition between the different sectors was very complex and could dynamically change under the different scenarios. For example, an increase in hydropower and flow to sea could lead to a decrease in irrigation water, but an increase in irrigation water did not necessarily reduce hydropower and flow to sea. Water for hydropower generation and salt tide alleviation were obviously affected by the total offstream water use, but there existed no obvious water competition between these aspects in general. However, when offstream water use remained stable, a competitive relationship was observed between hydropower and flow to sea. Overall, the outcomes of this study could be of great significance to further analyse the WFEN in other regions.

This study proposes a performance appraisal framework for agricultural water distribution systems based on the Water-Food-Energy Nexus perspective. To analyze and evaluate agricultural water distribution systems with this framework, various methods of improving the operational management were developed and tested under the conventional and water shortages in operational scenarios. The Water-Food-Energy Nexus indicators were then calculated for performance appraisal of the water distribution systems in a study area, located in central Iran. The results indicated that by upgrading the manual operation to an automatic control system, gave the best results from the nexus indicators perspectives. The Bayesian Network model was used to present a probabilistic approach that could assist managers and decision-makers in evaluating the performance of the system, based on the nexus perspective. For this purpose, various configurations of the Bayesian Network structures were developed based on an export-oriented approach, and the most appropriate model was determined for the test case. The calibration and validation process of the selected configuration approved a high accuracy in fulfilling the objective of the study. The developed framework can be employed as a decision support model to prioritize options for modernizing agricultural water distribution systems.

Seagrass meadows ecosystem engineering effects are correlated to their density (which is in turn linked to seasonal cycles) and often cannot be perceived below a given threshold level of engineer density. The density and biomass of seagrass meadows (Z. marina) together with associated macrophytes undergo substantial seasonal changes, with clear declines in winter. The present study aims to test whether the seasonal changes in the density of recovering seagrass meadows affect the benthic food webs of the southern Baltic Sea (Puck Bay). It includes meiofauna, macrofauna and fish of vegetated and unvegetated habitats in summer and winter seasons. Two levels of organization have been tested – species-specific diet preferences using stable isotopes (δ13C, δ15N) in Bayesian mixing models (MixSIAR) and the community-scale food web characteristics by means of isotopic niches (SIBER). Between-habitat differences were observed for grazers, as a greater food source diversity in species from vegetated habitats was noted in both seasons. Larger between-habitat differences in winter were documented for suspension/detritus feeders. The community-wide approach showed that the differences between the habitats were greater in winter than in summer (as indicated by the lower overlap of the respective isotope niches). Overall, the presence of seagrass meadows increased ecological stability (in terms of the range of food sources utilized by consumers) in the faunal assemblage, while invertebrates from unvegetated areas shifted their diet to cope with winter conditions. Therefore, as a more complex system, not sensitive to seasonal changes, Z. marina meadows create a stable habitat with high resilience potential.

The widespread uncertainty regarding future changes in climate, socioeconomic conditions, and population growth have increased interest in water-energy-food-ecology nexus-based frameworks in relation to the analysis of water resources. A challenge for modeling the water-energy-food-ecology nexus is how to reduce the multidimensional and codependent uncertainties and measure the complicated casual relationships effectively. We propose a methodological solution to the problem, and this solution is demonstrated in this case as an extension to the previous water resource optimization framework. We coupled the water-energy-food-ecology nexus into the Bayesian network, which provides a formal representation of the joint probabilistic behavior of the system, and the method was applied to water resource use analysis and management in the Syr Darya River basin, a transboundary and endorheic basin that has contributed to the Aral Sea ecological crisis as a result of unreasonable water use. The annual scale data of four periods, 1970–1980, 1980–1991, 1991–2005, and 2005–2015, were introduced into the Bayesian network. Before the disintegration of the Soviet Union, the amount of water inflow into the Aral Sea was sensitive to increases in irrigation for agricultural development, increases in water storage of the upstream reservoirs and stochastic runoff. After the disintegration of the Soviet Union, the amount of water inflow into the Aral Sea was sensitive to the inefficient irrigation water use in the downstream areas of Uzbekistan and Kazakhstan and the water storage of the reservoir located upstream of Kyrgyzstan. The change resulted from unresolvable disputes between water use for power generation in the upstream area and irrigation in the downstream area. Comprehensive scenario analysis shows that, in the short term, it would be useful to improve the proportion of food crops, improve the efficiency of water use in relation to salt leaching and irrigation, and prevent drought damage. In the long term, based on the increased use of advanced drip irrigation technology from 50% to 80%, the annual inflow into the Aral Sea will increase significantly, reaching 6.4 km³ and 9.6 km³, respectively, and this technology is capable of ameliorating the ecological crisis within the basin.

The risks associated with water-energy-food (WEF) nexus can be determined by analyzing the three resources as a system and subsequently measuring the probability of resource shortages in this system under the influences of external factors such as climate, economy, and society. In this study, a Bayesian network (BN) model was used to construct the supply-demand risk assessment framework, identify the risk factors during different periods in the Beijing-Tianjin-Hebei (BTH) region, and simulate the probability of supply-demand risk under different scenarios. The results showed that (1) the WEF supply-demand risk in the BTH region originated from not only core system factors, but also from external system factors. (2) The factors affecting the WEF supply-demand risk varied significantly over time. In the tortuous development stage (1989–1998), industrial and agricultural water use intensity were highly important. In the rapid development stage (1999–2008), the population growth rate became the main factor. In the coordinated development stage (2009–2018), the water and food supply-demand risks became increasingly important. (3) In scenarios where representative climate-economy-society factors (rainfall, gross domestic product, and population) were in high-high-high, medium-high-high, low-medium-high, and low-high-high, respectively, the probability of WEF supply-demand risk increased significantly. This probability was effectively reduced in medium-low-low, medium-medium-low, and medium-high-low scenarios. Using BN to study the WEF supply-demand risk allows for an intuitive understanding of the interaction mechanisms among variables in the WEF nexus. This approach also enables the design of more realistic scenarios, thus providing a reference for the formulation of early risk warning mechanisms.