It is widely acknowledged that waterbodies are becoming increasingly affected by a wide range of drivers of change arising from human activity. To illustrate how this can be quantified a linked modelling approach was applied in the Thames river basin in southern UK. Changes to river flows, water temperature, river and reservoir quality were predicted under three contrasting future "storylines"; one an extension of present day rates of economic development, the others representing more extreme and less sustainable visions. Modelling revealed that lower baseflow conditions will arise under all storylines. For the less extreme storyline river water quality is likely to deteriorate but reservoir quality will improve slightly. The two more extreme futures could not be supported by current management strategies to meet water demand. To satisfy these scenarios, transfer of river water from outside the Thames river basin would be necessary. Consequently, some improvement over present day water quality in the river may be seen, and for most indicators conditions would be better than in the less extreme storyline. However, because phosphorus concentrations will rise, the invoked changes in water demand management would not be of a form suitable to prevent a marked deterioration in reservoir water quality.

A need for multi-functional assessment tools evaluating trade-offs and co-benefits for various types of Nature-Based Solutions (NBS) has been increasingly identified in recent years. Methodologically, concepts for a tool are presented which include quantifying the demand and potential for NBS to enhance ecosystem service (ES) provision, and linking ESs to readily quantifiable and legislatively-relevant environmental quality indicators (EQIs). The objective of tool application is to identify optimal NBS placement across a diverse set of socio-environmental indicators, whilst also incorporating issues of relative location of areas of implementation and benefit accrual. Embedded within the tool is the importance of evaluating outcomes in terms of economic benefits and of sustainable development goals. The concepts are illustrated with simplified examples, relating to the case of implementing urban forestry as an exemplar NBS. By summarising the knowledge base it is demonstrated that benefits of NBS are substantially scale-dependent in two main respects; those of extent and proximity to receptors. Evaluation tools should be capable of quantifying scale-dependence. The substantive importance of these considerations and how their dynamics vary between indicators and services is illustrated graphically through schematic functions. When developed, the tool should be used as a focus for consultation and co-design to pinpoint the size of NBS necessary to achieve a sufficient level of benefit for a particular receptor. This could be measured against target levels of benefit for each indicator, distinguishing between primary intended outcomes and those co-benefits or trade-offs that are secondary or unintended.

Nature-based solutions (NbS) provide direct benefits to people who live in areas where these approaches are present. The degree of direct benefits (thermal comfort, reduced flood risk, and mental health) varies across temporal and spatial scales, and it can be modelled and quantified. Less clear are the indirect benefits related to opportunities to learn about the environment and its influence on personal behaviour and action. The present study, based on survey data from 1955 participants across 17 cities worldwide, addressed whether participation in NbS through two types of interactions (a passive learning experience about NbS and a more active experience based on Citizen Science) stimulates motivation and willingness to be more environmentally sustainable. Over 75% of participants improved their understanding of environmental sustainability and were highly motivated and more confident in their ability to improve sustainability in their local environment/nature. Similar percentage improvements arose from both types of activity across all cities. Those NbS that had elements of both blue and green infrastructure rated higher than those that had predominantly green NbS. Interestingly, a large percentage of the participants did not live near the NbS that were the focus of these activities. This indicated that expected spatial limitations between benefit and recipient may be overcome when dedicated programmes involve people in learning or monitoring NbS. Therefore, opportunities have arisen to expand inclusion from the immediately local to the larger community through participation and Citizen Science, with potential benefits to social cohesion and urban sustainability.

A modelling study was undertaken to quantify effects that the climate likely to prevail in the 2050s might have on water quality in two contrasting UK rivers. In so doing, it pinpointed the extent to which time series of climate model output, for some variables derived following bias correction, are fit for purpose when used as a basis for projecting future water quality. Working at daily time step, the method involved linking regional climate model (HadRM3-PPE) projections, Future Flows Hydrology (rainfall–runoff modelling) and the QUESTOR river network water quality model. In the River Thames, the number of days when temperature, dissolved oxygen, biochemical oxygen demand and phytoplankton exceeded undesirable values (>25°C, 4 mg L⁻¹ and >0.03 mg L⁻¹, respectively) was estimated to increase by 4.1–26.7 days per year. The changes do not reflect impacts of any possible change in land use or land management. In the River Ure, smaller increases in occurrence of undesirable water quality are likely to occur in the future (by 1.0–11.5 days per year) and some scenarios suggested no change. Results from 11 scenarios of the hydroclimatic inputs revealed considerable uncertainty around the levels of change, which prompted analysis of the sensitivity of the QUESTOR model to simulations of current climate and hydrology. Hydrological model errors were deemed of less significance than those associated with the derivation and downscaling of driving climatic variables (rainfall, air temperature and solar radiation). Errors associated with incomplete understanding of river water quality interactions with the aquatic ecosystem were found likely to be more substantial than those associated with hydrology, but less than those related to climate model inputs. These errors are largely a manifestation of uncertainty concerning the extent to which phytoplankton biomass is controlled by invertebrate grazers, particularly in mid-summer; and the degree to which this varies from year to year. The quality of data from climate models for generating flows and defining driving variables at the extremes of their distributions has been highlighted as the major source of uncertainty in water quality model outputs.