Joseph Alcamo

By approving the Sustainable Development Goals (SDGs) more than 190 countries have made a commitment to “ensure availability ... of water ... to all”. Yet the implications of this high-profile pledge are unclear because of the lack of an internationally accepted definition of “water availability”. Moreover, definitions, where they exist, are usually incomplete because they neglect the important factor of water quality. This is a significant omission because degraded water quality impedes the usage of water resources for drinking water, hygiene, irrigation, habitat for plant and animal communities, and other important uses. It can be argued that contaminated water withdrawals can be treated to make more water available, but this is not an affordable option for much of the Global South except for drinking water. Hence, degraded water quality genuinely reduces water availability.Until recently water quality could not be included in large-scale (regional to global) assessments of water availability because of the lack of appropriate data and tools. The situation is changing, however, with the development of a new class of large-scale water quality models. These models have broad geographic coverage and a fine enough grid to simulate water quality gradients along river networks.As an example, as part of a UNEP study, the WorldQual model estimated that pathogen pollution hindered the usage of approximately one-third of the total river network in Latin America, Africa, and Asia for safe bathing and hygienic purposes. Organic pollution was estimated to impede the usage of about one-seventh of this river network for fish production, and salinity pollution about one-tenth of the network for irrigation water supply. These and other preliminary results from the modelling community suggest that water quality should not be overlooked as a potentially important factor in large-scale assessments of water availability.To improve the performance of large-scale water quality models, and make them more reliable for including in water availability assessments, researchers will have to contend with some difficult challenges including (but not limited to):The problem of representativeness – A wide range of water quality parameters are relevant to uses of surface water and groundwater, and the challenge is to find a manageable set of representative parameters for analysis that are both measurable and calculable on the large-scale.The problem of validation – There is a paucity of data available for validating large-scale water quality models, and in some parts of the world the coverage and frequency of data collection is declining rather than increasing. The problem of characterising anthropogenic fluxes of contaminants – The data necessary for determining fluxes of contaminants into the water environment (e.g. data on wastewater discharges) are either not accessible or very incomplete in many countries. Finally, a particularly high priority for going forward is to establish collaborations between researchers assessing the quantity of water available (for example, under climate change) and those assessing the quality of water. These collaborations are a prerequisite for developing a more comprehensive and realistic concept of water availability.

The endeavor for greater food security has caused trade-offs between increasing agricultural production and conserving habitat of threatened species. We take a novel approach to analyze these trade-offs by applying and comparing three systems methods (systems diagrams, influence matrices, and land use modeling) in a case study of Uganda. The first two methods were used to scope out the trade-off system and identify the most important variables influencing trade-offs. These variables were agricultural yield, land governance processes, and change in land use and land cover. The third method was used to quantify trade-offs and evaluate policy scenarios to alleviate them. A reference scenario indicated that increasing agricultural production by expanding agricultural land provided food for 79% more people in 2050 (compared to 2005) but with a 48% loss of habitat of threatened species. A scenario assuming strong investments to augment agricultural yield increased the number of people fed in 2050 up to 157%, while reducing the loss of habitat down to 27%. We use a novel “trade-off coefficient” for a consistent comparison of scenario results. A scenario assuming yield improvement and ring-fencing protected areas reduced the trade-off coefficient from − 0.62 in the reference case to − 0.15. This coefficient can be used as a common basis to compare results from different trade-off studies. It was found that the three systems methods are useful, but have limitations as stand-alone tools. Combining the methods into a single methodology increases their collective utility by maximizing the transparency and comprehensiveness and potential stakeholder engagement of a trade-off analysis.

Actions to transform food systems are key to addressing biodiversity loss.

The topic of SDG interactions is a relatively new research area with many knowledge gaps. Some of these gaps are addressed in this summary of a Special Feature of Sustainability Science, including new findings and emerging issues on (1) the characteristics of SDG interactions; (2) methods/methodology to analyse these interactions; and (3) the elaboration of drivers that influence SDG synergies. The importance of scale is clear in two emerging issues. First, there is evidence of a disconnect between national planning for SDGs and their implementation at the local scale which is leading to SDG trade-offs between these scales. Second, the concept of a “critical transition zone” is introduced where SDG trade-offs pose a particular challenge to SDG implementation. These are areas (e.g., peri-urban and forest margin areas in the Global South) undergoing rapid biophysical and/or socio-economic changes and inhabited by populations especially vulnerable to these changes. While trade-offs occur among the SDGs, there are also many examples of synergies which provide opportunities for advancing multiple goals. To distinguish between synergies and the actions that exploit them, the term “synergy driver” is introduced to refer to policies and measures that positively advance two or more goals. Several examples of synergy drivers are presented, including sustainable global supply chains, people-centred early warning systems, and joint conservation-public health programmes. To make synergy drivers relevant to the broader policy community, the research community (working with stakeholders) should first consolidate knowledge about these drivers and then evaluate their effectiveness/applicability to different policy settings.