Emerging global threats, such as biological invasions, climate change, land use intensification, and water depletion, endanger the sustainable future of lakes and reservoirs. To deal with these threats, a multidimensional view on the protection and exploitation of lakes and reservoirs is needed. The holistic approach needs to contain not just the development of economy and society but also take into account the negative impacts of this growth on the environment, from that, the balance between the three dimensions can be sustained to reach a sustainable future. As such, this paper provides a comprehensive review on future opportunities and challenges for the sustainable development of lakes and reservoirs via a critical analysis on their contribution to individual and subsets of the Sustainable Development Goals (SDGs). Currently, lakes and reservoirs are key freshwater resources. They play crucial roles in human societies for drinking water provision, food production (via fisheries, aquaculture, and the irrigation of agricultural lands), recreation, energy provision (via hydropower dams), wastewater treatment, and flood and drought control. Because of the (mostly) recent intensive exploitations, many lakes and reservoirs are severely deteriorated. In recent years, physical (habitat) degradation has become very important while eutrophication remains the main issue for many lakes and ponds worldwide. Besides constant threats from anthropogenic activities, such as urbanization, industry, aquaculture, and watercourse alterations, climate change and emerging contaminants, such as microplastics and antimicrobial resistance, can generate a global problem for the sustainability of lakes and reservoirs. In relation to the SDGs, the actions for achieving the sustainability of lakes and reservoirs have positive links with the SDGs related to environmental dimensions (Goals 6, 13, 14, and 15) as they are mutually reinforcing each other. On the other hand, these actions have direct potential conflicts with the SDGs related to social and economic dimensions (Goals 1, 2, 3 and 8). From these interlinkages, we propose 22 indicators that can be used by decision makers for monitoring and assessing the sustainable development of lakes and reservoirs.

Though aquaculture plays an important role in providing foods and healthy diets, there are concerns regarding the environmental sustainability of prevailing practices. This study examines the trends and changes in fisheries originating from aquaculture production in Thailand and provides insights into such production’s environmental impacts and sustainability. Together with an extensive literature review, we investigated a time series of Thai aquaculture production data from 1995 to 2015. Overall, Thai aquaculture production has significantly increased during the last few decades and significantly contributed to socio-economic development. Estimates of total aquaculture production in Thailand have gradually grown from around 0.6 to 0.9 million tons over the last twenty years. Farmed shrimp is the main animal aquatic product, accounting for an estimated 40% of total yields of aquaculture production, closely followed by fish (38%) and mollusk (22%). Estimates over the past decades indicate that around 199470 ha of land is used for aquaculture farming. Out of the total area, 61% is used for freshwater farms, and 39% is used for coastal farms. However, this industry has contributed to environmental degradation, such as habitat destruction, water pollution, and ecological effects. Effective management strategies are urgently needed to minimize the environmental impacts of aquaculture and to ensure it maximally contributes to planetary health. Innovative and practical solutions that rely on diverse technology inputs and smart market-based management approaches that are designed for environmentally friendly aquaculture farming can be the basis for viable long-term solutions for the future.

As one of the earth's key ecosystems, rivers have been intensively studied and modelled through the application of machine learning (ML). With the amount of large data available, these computer algorithms are ever increasing in numerous fields, although there is ongoing scepticism and scholars still question the actual impact and deliverables of algorithms. This study aims to provide a systematic review of the state‐of‐the‐art ML‐based techniques, trends, opportunities and challenges in river research by applying text mining and automated content analysis. Unsupervised and supervised learning have dominated river research while neural networks and deep learning have also gradually gained popularity. Matrix factorisation and linear models have been the most popular ML algorithms, with around 1300 and 800 publications on these topics in 2020 respectively. In contrast, river researchers have had few applications in multiclass and multilabel algorithm, associate rule and Naïve Bayes. The current article proposes an end‐to‐end workflow of ML applications in river research in order to tackle major ML challenges, including four steps: (1) data collection and preparation; (2) model evaluation and selection; (3) model application; and (4) feedback loops. Within this workflow, river modellers have to balance numerous trade‐offs related to model traits, such as complexity, accuracy, interpretability, bias, data privacy and accessibility and spatial and temporal scales. Any choices made when balancing the trade‐offs can lead to different model outcomes affecting the final applications. Hence, it is necessary to carefully consider and specify modelling goals, understand the data collected and maintain feedback loops in order to continuously improve model performance and eventually reach the research objectives. Moreover, it remains crucial to address the users' needs and demands that often entail additional elements, such as computational cost, development time and the quantity, quality and compatibility of data. Furthermore, river researchers should account for new technologies and regulations in data collection and protection that are transforming the development and applications of ML, most notably data warehouse and information management with multiple‐cycles that are becoming a cornerstone of the integration of ML in decision‐making in river and ecosystem management.