Climate change projections point to longer or more frequent (or both) meteorological droughts in some regions by 2100 but there remain substantial uncertainties as to how rainfall and soil moisture deficits might translate into prolonged periods of reduced streamflow and groundwater levels (IPCC, 2014). This and other pressures affect and will continue to affect UK water availability into the future. Climate change could cause a decline in the amount of water available for supply, particularly in summer months if lower average rainfall coincides with increased temperatures (e.g. Murphy et al., 2009). Water demand may also be sensitive to climate variability, although few studies have examined this aspect (e.g. Parker and Wilby, 2013). Population growth, alongside a greater number of single-occupancy households will greatly affect water demand, and this stresses the need for greater water efficiency. Water resources may therefore increasingly need to be enhanced and managed through new supply or demand management options. Supply options may include storage reservoirs, inter-basin transfers, desalination plants, effluent reuse, and groundwater and river abstractions whilst demand management options may include water meters, water saving devices, efficient appliances, rainwater collection systems, and grey water recycling.

A number of studies have investigated the impact of climate change on water resources, addressing the associated uncertainties, using both deterministic and probabilistic projections of climate change. For example, in the UK these include Wilby and Harris (2006), New et al. (2007), Dessai and Hulme (2007), Christensen and Lettenmaier (2007), Vidal and Wade (2009), Manning et al. (2009), and Lopez et al. (2009). Burke and Brown (2010) indicated that an 11 member HadRM3 perturbed physics ensemble showed uncertainty as to whether drought occurrence will decrease or increase across the UK by the end of the 21st century (consistent with earlier results from a multi-model ensemble; Blenkinsop and Fowler, 2007) although they identify a predominant tendency to the latter. Burke and Brown (2010) demonstrate similar results, with relatively little spatial variation across the UK. Such studies show the sensitivity of projections to climate model structure and parameterization and suggest that methods to downscale climate information can result in large sources of uncertainty in future river flows. However, a “cascade” of uncertainties arise when considering climate change impact assessments for decision making (Jones, 2000). Wilby (2005) showed that uncertainties associated with impact studies arise from model structure, choice of model calibration period, choice of parameter sets, as well as climate scenarios and downscaling methods.

In this study, we use the UK Climate Projections (UKCP09) which provides an ensemble of climate model outputs that capture a number of important uncertainties in climate model parameterization and structure (Murphy et al., 2009). The UKCP09 outputs have been used to consider: flood risk (Cloke et al., 2013; Kay and Jones, 2012), changes in precipitation and potential evapotranspiration (PET) in upland river catchments (Thompson, 2012), sediment yield in catchments (Coulthard et al., 2012), urban heat island effects (Lee and Levermore, 2013), and overheating in buildings (Patidar et al., 2014; Jenkins et al., 2014). Their release and availability has also enabled better assessment of uncertainties in projections of water availability in a changing climate (e.g. Christierson et al., 2012; Harris et al., 2013; Warren and Holman, 2012). Using the UKCP09 projections, Harris et al. (2013) found that for the North Staffordshire Water Resource Zone uncertainty in climate model parameterization causes a greater proportion of uncertainty in estimates for the 2080s of change in overall flow and water shortage per year than emission scenario choice. Whitehead et al. (2006), Wilby and Harris (2006), Dessai and Hulme (2007), and New et al. (2007) have all conducted end-to-end assessments of propagation of uncertainties in adaptation assessments. Understanding the range of uncertainties when assessing future water shortages in the face of climate change will enable policies and strategies to be designed that are robust to the full range of plausible futures. Methods such as robust decision making (RDM) provide a quantitative approach to facilitate decision-making under a range of assumptions and uncertainties. RDM has been used to develop long-range water management plans in the US (Groves et al., 2008; Lempert and Groves, 2010). Matrosov et al. (2013) applied both RDM and info-gap decision theory to consider uncertainties to proposed water supply portfolios for the Thames Basin. Borgomeo et al. (2014) demonstrated a methodology based on UKCP09 that used non-stationary probabilistic climate scenarios to aid risk-based water resource management. More recently the decision-scaling method (Brown et al., 2012) or climate stress testing (Brown and Wilby, 2012) has been applied to water resources systems. Multiple sources of climate information, climate projections, and stochastic assessments are used to evaluate risks (Brown et al., 2012) and subsequently applied to determine robust adaptation strategies (e.g. Whateley et al., 2014; Steinschneider et al., 2015).

This paper builds on and extends this previous research to assess current and future water resources risk by developing and integrating:

simulation models of precipitation, catchment hydrology, and water resource systems within an uncertainty analysis framework;

Between 2003 and 2006, England and Wales reported the third lowest rainfall since 1932–1934; the Thames and the south-east experienced exceptional regional rainfall deficits. Of particular importance was the disproportionate concentration of overall rainfall deficit in the winter and spring, when typically modest evaporation losses allow the bulk of reservoir replenishment and aquifer recharge (Marsh, 2007). Drought Severity Index analysis for the Thames catchment, as for other water resource regions in the south, shows an historical increase in drought intensity and frequency of drought months in both wet and dry seasons, as well as frequency of drought events with persistence of at least 3 or 6 months (Rahiz and New, 2013). In this study, the combined projection of increased PDD and PET during summer months and lasting further into autumn, highlight the potential for increased frequency of such climate-driven water resource pressures in the future.

The largest decreases in river flows are projected for September to November; with the greatest effects being decreases in low flows. At the outlet of the catchment projected mean change in Q90 is a 14 % decrease, with a similar value, 15 % for Q95 (SCN20). However, the changes are much greater for SCN50 – 33 % for Q90 and 37 % for Q95. Manning et al. (2009), found mean decreases in Q95 of 45 % using the HadRM3H model and 37 % using the HadRM3P model for the 2080s, using the medium-high scenario for the Thames. In their UK-wide study, Christierson et al. (2012) highlight that the largest flow decrease was found in the Thames, Anglian, and Severn river basin regions, with a high probability assigned to decline in summer flows. They also conclude that the dispersion of distributions in projected monthly flows for the Thames catchment to be larger than the range of natural variability.

There have been five major water resource droughts in the Thames catchment over the last 90 years (Thames Water, 2014): 1920–1921; 1933–1934; 1943–1944; 1975–1976; and 2010–2012. Most recently, the 24-month period from April 2010 to March 2012 was the driest in the 128-year record for the Thames catchment. During this period, intensive media campaigns highlighted the drought and promoted water efficiency; in 2012 both a Temporary Use Ban and Non-Essential Use ban were implemented (i.e. Level of Service 3 restrictions). Our results (see Fig. 8a) clearly show that existing water supplies are sensitive to changing climate. In particular, the requirement for DS3 and DS4 measures is projected to increase by the 2020s and more so by the 2050s. Similarly, Darch et al. (2011) found their central estimates of supply–demand deficiency for the London Water Resource Zone may increase from 51 ML day-1 under the 2020s medium emissions scenario to 516 ML day-1 under the 2080s high emissions scenario, albeit with large uncertainties. Although they considered a wider range of climate projections, they did not consider how such impacts may be compounded by population growth; resulting in increasing demand for resources.

Population growth, especially in London and the south-east will inevitably place increased pressure on already limited water resources. By 2031, the GLA (2011) expect London's population to increase by 16–21 %. Our analysis demonstrates the potential impacts of both climate change and population growth on water availability. Population projections are available up to 2031, beyond which we have extrapolated the average growth rate to 2050. It could be argued that this may be a conservative estimate as London continues to regenerate, expand, and invest in major infrastructure projects to attract increased investment and agglomerations of organizations, and hence ultimately population. However, both climate changes and population growth will occur simultaneously, therefore the starting point for assessing the benefit of any supply and demand adaptation measures needs to be based upon the projections shown in Fig. 8c. When comparing the expected frequency of level of service/demand saving measures from Thames Water (see Table 1), results show that targets are increasingly less likely to be met. For example, currently the target is to never implement DS4 measures, but our analysis indicates that these may be required once every 2 years by the 2020s and once every year by the 2050s.

Globally, the greatest demand for water is driven by agriculture and industry; however, in the UK, given reduced industrial and mining demand for water, more emphasis has been placed on the demand management of potable water (McDonald, 2007). Supply-side solutions have dominated water management, with little attention given to long-term demand forecasting. Parker and Wilby (2013) reviewed approaches to water demand estimation and forecasting for daily–seasonal and yearly–decadal timescales for household water use. They concluded that little consideration has been given to UK household water demand estimation and forecasting under a changing climate. However, water demand management is increasingly recognized as a “low regret” adaptation from both a financial and environmental point of view, which can be implemented at a range of scales from individuals and households to communities. Water meters have been shown to decrease water use by 10–15 % per household (GLA, 2011), as well as improve energy efficiency, given the substantial proportion of energy used to heat water within a household. The GLA have ambitious targets for the installation of water meters in London properties (all houses and blocks of flats by 2020 and all individual flats by 2025; GLA, 2011). There are no guarantees on the uptake of demand saving measures such as water meters, grey water recycling, or water efficient appliances; however, our analysis (see Fig. 10) has demonstrated that even small reductions in per capita demand can reduce the median frequency of DS4 measures, e.g. by 50 % by the 2020s. A 35 % reduction in per capita demand by 2020, which is perhaps unrealistic, would eliminate the risk of drought orders. The Future Water Strategy (Defra, 2008) suggests a target of reducing per capita usage from 150 to 130 L day-1, a 13 % reduction by 2030. By 2050, even a 35 % reduction in per capita demand is no longer effective and new supply options need to be considered.

The London Water Resource Zone supply–demand deficit is currently finely balanced and it is recognized that a new supply resource will be required by the end of the 2020s (Thames Water, 2014). The UK's first desalination plant built in the Thames Gateway became operational in 2010. Our results show that this new resource increases the reliability of supply through the 2020s; however, by the 2050s, consistent with Borgomeo et al. (2014), our analysis shows that further new resource may be required. Here, we go further to consider additional supply options. A new reservoir with a storage capacity of 100 million m3 is a beneficial new resource in the 2050s; however, in addition to the socioeconomic costs of new schemes, climate sensitivity of both supply and demand reduction options also need to be considered. For instance leakage reduction and artificial recharge are not as sensitive to externalities as new storage options which require adequate precipitation or personal usage reductions given a warmer climate. In their 2011 study, Darch et al. found that the cost effectiveness of new reservoir options for the Thames catchment are sensitive to assumptions about climate change. Compulsory metering and leakage reduction schemes were selected under all of the scenarios, with a new reservoir option becoming plausible by the 2050s under medium emissions scenarios. By the 2080s, it was found that a strategic transfer, e.g. Severn–Thames would also be necessary; alternatives such as indirect reuse and further desalination capacity were also considered but are much more expensive and carbon intensive (Darch et al., 2011).

Results from this study advocate the twin-track approach of demand reductions and new supply options to minimize the risk of severe imposed restrictions on water resources. Considering a plausible representation of future climate, demand scenarios and potential adaptation strategies will aid water managers' assessment of where vulnerabilities occur. Hall and Borgomeo (2013) proposed a framework to test strategies for adapting to risks that enables testing large numbers of synthetic hydrological sequences and allows exploration of different sources of uncertainty including climate, catchment responses, and demands. In their case study on Adelaide's southern water supply system, Beh et al. (2015a, b) and Paton et al. (2014) demonstrate a multi-objective evolutionary algorithm framework to consider the trade-offs between reducing greenhouse gas emissions while planning sustainable urban water supply systems. Applied to North Carolina, Zeff et al. (2014) investigated how more flexible and adaptable water supply portfolios can be implemented alongside financial mitigation tools to reduce trade-offs between fluctuations of revenues and costs of implementing new solutions. Haasnoot et al. (2014) demonstrate the development of adaptation pathways whereby environment and policy responses are analysed through time to develop an ensemble of plausible futures to support decision making under uncertainty. Applying the approach and outcomes from this research in such risk frameworks would be valuable in considering costs, benefits, and trade-offs of adaptation measures. This would facilitate adaptive strategies that are able to evolve as new information becomes available; this is particularly useful given climate model, demographic, and supply uncertainty.

This study has advanced understanding of the potential future water resource risk and possible adaptation options for managing these risks for the Thames catchment. However, the study has a number of limitations, in particular one model chain and hence uncertainties were considered; multiple models and methods would result in further confidence in the results and interpretations. We used the UKCP09 probabilistic climate scenarios only for the medium emission scenario, for two time periods; although Harris et al. (2013) indicate that for the 2080s the uncertainty in the UKCP09 PPE is the cause of a greater proportion of uncertainty in flow and water shortage probability than is caused by the emissions scenario. We used only one hydrological model, CATCHMOD, and one parameter set, although this has been extensively tested for the Thames catchment (e.g. Davis, 2001; Wilby, 2005; Wilby and Harris, 2006; Manning et al., 2009). Wilby and Harris (2006) showed how both choice of hydrological model and choice of model parameters can affect the outcome of the modelling study. We have only considered the impacts of climate change and demand change on water resource availability at defined points in the future, i.e. 2020s and 2050s; however, there is a growing practical interest on how changes play out throughout a planning horizon such as an Asset Management Plan period. For this a transient implementation of the single-site NSRP model and Climatic Research Unit (CRU) weather generator (Burton et al., 2010b; Blenkinsop et al., 2013) could be implemented (e.g. Goderniaux et al., 2011).

The application of a sequence of models, including an extension of the UKCP09 weather generator that generates downscaled, probabilistic projections of rainfall on a grid over the Thames catchment, indicates that the hazard of inadequate water supply is expected to increase as a function of both climatic and socioeconomic drivers. Here we show that these hazards can be managed most effectively through a portfolio of adaptation measures.

Population growth exhibits a greater contribution to drought risk than climate projections. An extreme reduction of 35 % in daily per capita allocation would be necessary to offset application of drought orders by 2020. However, a relatively small decrease would have a significant impact, yet moving towards 2050 the need for new supply options could intensify. We found that increased supply from various adaptation options may compensate for increasingly variable flows; however, without reductions in overall demand for water resources such options will not be sufficient to adapt to both climate change projections and a growing population. For example, a 100 % reduction in the median number of DS4 days by 2050 can be achieved through leakage reduction and a new storage reservoir. An additional 37.5 % improvement can be obtained by adding the contribution of the desalination plant to this portfolio. A 15 % reduction in overall demand in 2020 and 30 % in 2050 has a greater impact on reducing the frequency of DS4 days than any of the individual or combinations of supply options. Water demand reductions are clearly important in reducing water resource deficits; however, given projected population growth these will need to be significant to offset demand increases alongside climate change.

Like other cities, London is at risk and needs to adapt to a range of climate-related hazards, e.g. flooding, urban heat, and subsidence (Hallett, 2013) that need to be managed synergistically to avoid any potential conflicts (Dawson, 2007). Many urban areas have set greenhouse gas emission reduction targets (Heidrich et al., 2013). Reducing water demand can reduce energy consumption, as water use in the home accounts for 89 % of all carbon emissions resulting from water use (Environment Agency, 2008). Conversely, the introduction of energy intensive adaptation options such as desalination plants or inter-basin transfers may conflict emission reduction targets.

Given the typical investment timescale to plan, approve, and implement changes, decisions for water management infrastructure development can have consequences over long timescales (Hallegatte, 2009). When considering any major infrastructure investment and development, such as a new reservoir, a range of environmental, economic, and social consequences need to be critically analysed. The approach demonstrated here can be used to assess a range demand and supply adaptations that can be implemented and be effective on short and long timescales to make robust decisions about water resource management.

This study of the Thames catchment and subsequent analysis has highlighted the following priorities for future research. Firstly, which will be addressed in a forthcoming paper, is an extension of the climate scenarios to include the 2080s time period, coupled with the application of a spatial weather generator feeding a physically based, spatially distributed hydrological model which will allow better representation of both the climatological and land cover heterogeneity of the catchment. Furthermore, it will enable changes in land cover, i.e. increasing urban areas to be considered. Secondly, recognising the importance of groundwater in the Thames catchment and hence the potential impact that multi-season droughts may have on the area, further research is needed to understand how trends in such phenomena may affect or influence the choice of adaptation options. This, alongside a third research priority looking more generally about the sequencing of implementation of adaptation options over indicative planning horizons taking account of trade-offs with reducing greenhouse gas emissions or investment portfolios could make use of more robust decision making frameworks under uncertainty such as those proposed by for example Beh et al. (2015b) or Haasnoot et al. (2014).