Climate Change Patterns over Europe AQUATERRA / PRUDENCE Project Results Aidan Burton, Stephen Blenkinsop, Vassilis Glenis, Claire Walsh, Isabella Bovolo, Lucy Manning, Hayley Fowler, Jim Hall, Chris Kilsby

Downscaling methods Relevance to groundwater Rainfall Transpiration & Evaporation Vegetation Soil Aquifer Rainfall Surface runoff Recharge Non-linearity of recharge

Downscaling methods Dynamic downscaling Nested Regional Climate Models (RCMs) Resolution: 50 km / 25 km Domain: size of Europe or India Physical basis Computationally expensive Still issues with orographic effects, biases and extremes Multiple GCM-RCM ensembles Why ?

Statistical Downscaling General Circulation Models (GCMs) Downscaling methods Statistical Downscaling General Circulation Models (GCMs) e.g. HadAM3H, ECHAM4 Regression methods Weather/circulation classification Stochastic weather generators Dynamical Downscaling Regional Climate Models (RCMs) e.g. HIRHAM, RCAO Weather scenarios Change Factors

PRUDENCE Project PRUDENCE EU FP5 (2001 - 2004) Various GCM/RCM combinations with European domain SRES A2 / B2 emissions Available for AQUATERRA project (2004 - 2009) PRUDENCE superseded by ENSEMBLES project (2004 - 2009)

European climate projections A2 emissions PRUDENCE Project European climate projections A2 emissions Comparing 1961-1990 with 2071-2100 Temperature: increases winter and summer Winter Summer Increase (oC) Christensen (2007)

European climate projections A2 emissions PRUDENCE Project European climate projections A2 emissions Comparing 1961-1990 with 2071-2100 Precipitation: Increasing Winter to North of Med. Decreasing Summer & Med. in Winter Winter Summer Change (%) Christensen (2007)

European hydrological impacts PRUDENCE Project European hydrological impacts A2 emissions Summer increased: drought, maximum precipitation and interannual variability Complex impacts on hydrological cycle Baltic Rhine Future Control Control Future Graham et al. (2007)

AQUATERRA Results Case study: Ebro River Basin 85500 km2 Elevation to 3400m Precipitation mean 620 mm/y centre ~ 320mm/y mountains > 2000mm/y PE 700mm/y Bovolo et al. (2011)

AQUATERRA Results Case study: Ebro River Basin Bovolo et al. (2011) Long term – 10 months Bovolo et al. (2011)

AQUATERRA Results Case study: Ebro River Basin Methodology: bias correction of RCM Reduced water availability, especially in Summer - when peak demand for irrigation Small increase in rain in winter Suggests insufficient dam storage to support irrigation Long term – 10 months Bovolo et al. (2011)

Stochastic downscaling EARWIG EA Rainfall and Weather Impacts Generator Kilsby et al. (2007) Daily rainfall, T, RH, wind, sunshine and PET on 5km UK grid for control and future scenarios Change factor fields: rainfall statistics: Mean, Variance, PD, Skew, Autocorrelation temperature statistics: Mean, SD

AQUATERRA Downscaling Developments EARWIG approach Single site Stationary time-slice climate scenarios Spatial Transient climate

AQUATERRA Results Spatial Stochastic Downscaling Dommel case study Dommel catchment, Netherlands and Belgium 1350 km2 Elevation maximum 76m Rainfall 792 mm/y PET 673 mm/y Agricultural / populated (0.6M) van Vliet et al. (2011) Climatic Change

AQUATERRA Results Spatial Stochastic Downscaling Dommel case study Methodology RCM ensemble SRES A2 emissions Based on EARWIG - Spatial rainfall model - Single site T and PE van Vliet et al. (2011) Climatic Change

AQUATERRA Results Spatial Stochastic Downscaling Dommel case study Control Future van Vliet et al. (2011) Climatic Change

AQUATERRA Results Spatial Stochastic Downscaling Dommel case study Control Future Irrigation rule: to match Potential Transpiration Visser et al. (2011) J. Contam. Hydrol.

AQUATERRA Results Spatial Stochastic Downscaling Dommel case study Control Future Irrigation rule: to match Potential Transpiration Visser et al. (2011) J. Contam. Hydrol.

AQUATERRA Results Spatial Stochastic Downscaling Dommel case study Annual Impacts on Keersop subcatchment 43km2 Modelled as natural, agricultural and irrigated Actual evaporation (+8%) Transpiration (+19%) Increased irrigation (+1100%) from 3 to 36mm/y 77% decrease in net runoff from irrigation Surface runoff from 3mm/y to 1mm/y Groundwater recharge (-38%) Discharge (-26% to -46%) Likely irrigation supply problem in Summer Irrigation rule: to match Potential Transpiration Visser et al. (2011) J. Contam. Hydrol.

AQUATERRA Results Transient groundwater impacts Geer Case study Apply change factors to observed statistics Transient Weather Generator PRUDENCE Projections Groundwater model Develop transient change factors Rainfall is 10, 25, 75, 90%iles and mean of each RCM. Groundwater – is 2 RCMs compared against a stationary control climate. 95% confidence interval. Doesn’t include model – param variation – but does include physics Goderniaux et al., WRR

Probabilistic Scenarios UKCP09 Perturbed physics & emulated ensemble 25km RCM 7 future time slices 3 emissions scenarios Produces PDFs of projected climate Integrated with a stochastic weather generator Jones et al. (2009)

Stochastic downscaling Spatial Weather Modelling Apply change factors to observed statistics Spatial Weather Generator Change factors UKCP09 CATCHMOD rainfall runoff model AQUATOR water resource demand model Case study: Thames sub-catchments

Stochastic downscaling Spatial Weather Modelling Days’ Weir Feildes’ Weir

Stochastic downscaling Spatial Weather Modelling Days’ Weir Feildes’ Weir Control Future

Conclusions Availability of European climate projections in multi-model ensembles But limited range of emissions scenarios Localized sophisticated modelling of water resources and groundwater impacts Emergent fields - transient climate change scenarios - probabilistic climate change projections

ENSEMBLES Project Active 2004-2009 Probabilistic climate change projections Representation of modelling uncertainties Improved GCM / RCM ensemble Mainly A1B emissions scenario (medium non-mitigation) Transient RCM runs GCM physics uncertainties ENSEMBLES Final Report (2009)