1. 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

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

3. 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 ?

4. 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

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PRUDENCE superseded by ENSEMBLES project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

6. European climate projections A2 emissions
PRUDENCE Project
European climate projections A2 emissions
Comparing with
Temperature: increases winter and summer
Winter
Summer
Increase (oC)
Christensen (2007)

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

8. 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)

9. 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)

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

11. 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)

12. 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

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

14. 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

15. 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

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

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

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

19. 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.

20. 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

21. 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)

22. 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

23. Stochastic downscaling Spatial Weather Modelling
Days’ Weir
Feildes’ Weir

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

25. 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

26. 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)