How might climate change affect river flows across the Thames Basin? An area-wide analysis using the UKCP09 Regional Climate Model ensemble

Bell, V. A.; Kay, Alison L.; Cole, Steven J.; Jones, R. G.; Moore, Robert J.; Reynard, Nick

doi:10.1016/j.jhydrol.2012.04.001

Cited by 70 publications

(56 citation statements)

References 46 publications

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“…9), which lies at the tidal limit and has been recording since 1883; both observed (gauged) flows and naturalised flows (gauged flows adjusted to allow for the net impact of upstream abstractions and discharges 19 ) are available from the UK National River Flow Archive (nrfa.ceh.ac.uk). The catchment is very heterogeneous, particularly in terms of hydrogeology with significant areas of both responsive clay soils and much more slowly responding soils underlain by chalk or limestone aquifers 20 . The latter give the catchment a relatively high baseflow index of 0.64 (the proportion of flows derived from groundwater sources), and mean that antecedent conditions, and therefore temporal patterns of precipitation and temperature over multi-day to seasonal durations, are an important influence on river flows in the catchment.…”

Section: The Thames Catchmentmentioning

confidence: 99%

Human influence on climate in the 2014 southern England winter floods and their impacts

Schaller¹,

Kay²,

Lamb³

et al. 2016

Nature Clim Change

273

291

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Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. precipitation set a record (Fig. 3a). Sustained high precipitation amounts 60 during the whole winter led to this record, rather than a few very wet days, Human influence on climate in the 2014 Southern 61and none of the 5-day precipitation averages over the three winter months 62 was a record (Fig. 3b). Similarly, while Thames' daily peak river flows were 63 not exceptional, the 30-day peak flow was the second highest since 64 measurements began in 1883 ( Supplementary Fig. 10 to provide a conservative estimate of uncertainty. 106We consider January precipitation and SLP, with Southern England 107Precipitation (SEP) averaged over land grid points in 50º-52ºN, 6.5ºW-2ºE. 189In the large RCM ensemble, the best estimate for the overall change in risk of is an increase of 43%, with a range from no change to 164% increase 192 associated with uncertainty in the pattern of anthropogenic warming (Fig. 5d). rainfall that we simulate is less on timescales that dominate flooding in this 252 catchment, consistent with the mechanism being an increase in the frequency 253 of the zonal regime, and so, successions of strong but fast-moving storms. 254Outputs from CLASSIC are combined with information about the location of

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Section: The Thames Catchmentmentioning

confidence: 99%

Human influence on climate in the 2014 southern England winter floods and their impacts

Schaller¹,

Kay²,

Lamb³

et al. 2016

Nature Clim Change

273

291

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“…Metrics such as the Palmer drought severity index (PDSI; Palmer, 1965) use potential evapotranspiration (PET) as an input to represent AED, while many hydrological models such as Climate and Land use Scenario Simulation in Catchments (CLASSIC; Crooks and Naden, 2007) or Gridto-Grid (G2G; Bell et al, 2009), which also require an input representing AED, use a distinct form of the PET which includes the intercepted water from rainfall (this is described later in the text) which we hereby name PETI. While hydrological models can make use of high-resolution topographic information and precipitation datasets, they are often driven with PET calculated at a coarser resolution (Bell et al, 2011(Bell et al, , 2012Kay et al, 2015). Therefore, we have also created a 1 km × 1 km resolution dataset, the Climate Hydrology and Ecology research Support System Potential Evapotranspiration dataset for Great Britain (1961Britain ( -2012) (CHESS-PE; Robinson et al, 2015a), consisting of estimates of PET and PETI, which can be used to run high-resolution hydrological models.…”

Section: Introductionmentioning

confidence: 99%

Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data

Robinson

Blyth

Clark

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Observations of climate are often available on very different spatial scales from observations of the natural environments and resources that are affected by climate change. In order to help bridge the gap between these scales using modelling, a new dataset of daily meteorological variables was created at 1 km resolution over Great Britain for the years 1961-2012, by interpolating coarser resolution climate data and including the effects of local topography. These variables were used to calculate atmospheric evaporative demand (AED) at the same spatial and temporal resolution. Two functions that represent AED were chosen: one is a standard form of potential evapotranspiration (PET) and the other is a derived PET measure used by hydrologists that includes the effect of water intercepted by the canopy (PETI). Temporal trends in these functions were calculated, with PET found to be increasing in all regions, and at an overall rate of 0.021 ± 0.021 mm day −1 decade −1 in Great Britain. PETI was found to be increasing at a rate of 0.019 ± 0.020 mm day −1 decade −1 in Great Britain, but this was not statistically significant. However, there was a trend in PETI in England of 0.023 ± 0.023 mm day −1 decade −1 . The trends were found to vary by season, with spring PET increasing by 0.043 ± 0.019 mm day −1 decade −1 (0.038 ± 0.018 mm day −1 decade −1 when the interception correction is included) in Great Britain, while there is no statistically significant trend in other seasons. The trends were attributed analytically to trends in the climate variables; the overall positive trend was predominantly driven by rising air temperature, although rising specific humidity had a negative effect on the trend. Recasting the analysis in terms of relative humidity revealed that the overall effect is that falling relative humidity causes the PET to rise. Increasing downward short-and longwave radiation made an overall positive contribution to the PET trend, while decreasing wind speed made a negative contribution to the trend in PET. The trend in spring PET was particularly strong due to a strong decrease in relative humidity and increase in downward shortwave radiation in the spring.

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“…Other climate projections used in the study include 10 000 Change Factors of the 2009 UK Climate Projections (UKCP09), the 11 Spatially Coherent Projections (SCPs) of UKCP09 and the Future Flows (FF) downscaled data from the 11 HadRM3 runs. They are all valid sources of climate information and have been applied in various impact and adaptation studies (Bell et al, 2012;Wade et al, 2013). The UKCP09 10 000 Change Factors are based on a complex methodology that includes the 11 HadRM3 simulations, many simulations of the Hadley Centre global climate model (GCM), other GCMs and a Bayesian emulator (Murphy et al, 2010).…”

Section: Climate and Streamflow Scenariosmentioning

confidence: 99%

Water resource vulnerability: simulation and optimisation models

Hoang¹,

Dessai²

2017

Proceedings of the Institution of Civil Engineers - Engineering

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Approaches to adaptation to a changing climate in water resource planning have relied on both simulation and optimisation models. Simulation models project the impacts of climate change on water system performance, while optimisation models show the optimal system performance under climate change conditions. This study uses two water resource models to analyse a water resource system in Sussex (south-east England) under climatic and socioeconomic uncertainty. Overall, the simulation and optimisation models show structural model uncertainty. The simulation model highlights potential vulnerability in current operational practice, while the optimisation model shows that the current system could be vulnerable to climate change and demand growth even under the best-case scenario. The integrated scenarios in this study combine both climate scenarios from four different climate products over the periods of 2020s, 2030s and 2050s and socio-economic scenarios represented by different demand profiles. The results show that water demand quickly becomes a controlling factor once it increases by more than 35% from the 2007 baseline level. Both models demonstrate a gradual increasing risk of supply deficit in the 2020s and the 2030s. Water deficit risks vary widely in the 2050s and are highly dependent on the socio-economic scenarios.

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How might climate change affect river flows across the Thames Basin? An area-wide analysis using the UKCP09 Regional Climate Model ensemble

Cited by 70 publications

References 46 publications

Human influence on climate in the 2014 southern England winter floods and their impacts

Human influence on climate in the 2014 southern England winter floods and their impacts

Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data

Water resource vulnerability: simulation and optimisation models

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