Université de Liège

Université de Liège

Faculté des Sciences Appliquées

Département ArGEnCo

Architecture, Géologie, Environnement et Constructions

Secteur GEO³

Géotechnologies, Hydrogéologie, Prospection Géophysique

Impact of Climate Change on Groundwater Reserves

by Pascal GODERNIAUX

Thesis presented to the University of Liège in fulfilment of the requirements for the degree of

"Docteur en Sciences de l'Ingénieur"

January 2010

This research was carried out by:

Pascal GODERNIAUX

Research Fellow ("Aspirant du FNRS")

Hydrogeology and Environmental Geology Group ArGEnCo Department

University of Liège (ULg) Chemin des Chevreuils, 1 Building B52

4000 Liège BELGIUM

This research was financed by:

Fonds de la Recherche Scientifique – FNRS (Funds for Scientific Research – FNRS) Rue d'Egmont, 5

1000 Bruxelles BELGIUM

Board of examiners composed of:

Prof. CHARLIER Robert, Université de Liège (Belgium) – President Prof. DASSARGUES Alain, Université de Liège (Belgium) – Supervisor Dr. BROUYERE Serge, Université de Liège (Belgium) – Co-supervisor Prof. PIROTTON Michel, Université de Liège (Belgium)

Dr. BLENKINSOP Stephen, Newcastle University (United Kingdom) Prof. THERRIEN René, Université Laval (Canada)

Prof. VANCLOOSTER Marnik, Université Catholique de Louvain (Belgium)

Citation: Goderniaux P., 2010. Impact of Climate Change on Groundwater Reserves. PhD Thesis.

University of Liège, Faculty of Applied Sciences. Liège, Belgium. pp. 190.

S UMMARY

Estimating the impacts of climate change on groundwater represents one of the most difficult challenges faced by water resources specialists. One difficulty is that simplifying the representation of the hydrological system, or using too simple climate change scenarios often leads to discrepancies in projections. Additionally, these projections are affected by uncertainties from various sources, and these uncertainties are not evaluated in previous studies. In this context, the objective of this study is to provide an improved methodology for the estimation of climate change impact on groundwater reserves, including the evaluation of uncertainties. This methodology is applied to the case of the Geer basin catchment (480 km²) in Belgium.

A physically-based surface-subsurface flow model has been developed for the Geer basin with the finite element model HydroGeoSphere. The simultaneous solution of surface and subsurface flow equations in HydroGeoSphere, as well as the internal calculation of the actual evapotranspiration as a function of the soil moisture at each node of the defined evaporative zone, improve the representation and calibration of interdependent processes like recharge, which is crucial in the context of climate change. Fully-integrated surface-subsurface flow models have recently gained attention, but have not been used in the context of climate change impact studies.

This surface-subsurface flow model is combined with advanced climate change scenarios for the

Geer basin. Climate change simulations were obtained from six regional climate model (RCM)

scenarios assuming the SRES A2 greenhouse gases emission (medium-high) scenario. These

RCM scenarios were statistically downscaled using two different methods: the 'Quantile Mapping

Biased Correction' technique and a 'Weather Generator' technique. Both of them are part of the

most advanced downscaling techniques. They are able to apply corrections not only to the mean

of climatic variables, but also across the statistical distributions of these variables. This is

Summary

important as these distributions are expected to change in the future, with more violent rainfall events, separated by longer dry periods. The 'quantile mapping bias-correction' technique generate climate change time series representative of a stationary climate for the periods 2011- 2040, 2041-2070 and 2071-2100. The 'CRU' weather generator is used to generate a large number of equiprobable scenarios simulating full transient climate change between 2010 and 2085. All these scenarios are applied as input of the Geer basin model.

The uncertainty is evaluated from different possible sources. Using a multi-model ensemble of RCMs and GCMs enables to evaluate the uncertainty linked to climatic models. The application of a large number of equiprobable climate change scenarios, generated with the 'weather generator', as input of the hydrological model allows assessing the uncertainty linked to the natural variability of the weather. Finally, the uncertainty linked to the calibration of the hydrological model is evaluated using the computer code 'UCODE_2005'.

The climate change scenarios for the Geer basin model predict hotter and drier summers and warmer and wetter winters. Considering the results of this study, it is very likely that groundwater levels and surface flow rates in the Geer basin will decrease. This is of concern because it also means that groundwater quantities available for abstraction will also decrease. However, this study also shows that the uncertainty surrounding these projections is relatively large and that it remains difficult to state on the intensity of the decrease.

Keywords: Groundwater, Climate change, Geer basin, Chalk, Integrated model,

HydroGeoSphere, Uncertainty, Stochastic scenarios, Weather generator, UCODE_2005.

A CKNOWLEDGEMENTS

This thesis is the achievement of three years work, during which many people supported and helped me. I would like to acknowledge all of them gratefully.

Special thanks to Alain Dassargues and Serge Brouyère who supervised this thesis. Thank you to them for all advices and discussions we had together during these years. Thank you for the time they spent to read this manuscript and give me some remarks and corrections. I also would like to acknowledge them for their important support when I applied for a position of 'research fellow' at FNRS, in 2006. I also remember the times we spent together in meetings and conferences. These times were both very instructive and pleasant. Finally, I would like to thank Alain Dassargues for proposing me to join his team and giving us the means to carry out research in a pleasant work environment.

Thank you to Robert Charlier, Michel Pirotton, René Therrien, Stephen Blenkinsop and Marnik Vanclooster, who have accepted to join the jury of this thesis and to take time to read this report.

I am very grateful to René Therrien, Daniela Blessent and Jean-Michel Lemieux for helping me to use 'HydroGeoSphere'. Thank you for their precious advices and recommendations. Thank you to René Therrien who allowed me to spend 3 months in his team at University Laval (Québec, Canada). Thanks to him, his family and Daniela Blessent for their warm welcome in Québec.

Similarly, special thanks to Hayley Fowler, Stephen Blenkinsop and Aidan Burton for the work

we have performed together to generate climate change scenarios for the Geer basin. Thank you

to them, as well as Isabella and Micol for the time spent in Newcastle. Thank you to Mary Hill

for giving me the opportunity to teach a short course with her and for answering very quickly to

my e-mails and questions about 'UCODE_2005'.

Acknowledgements

I am grateful to FNRS, which funded this research. FNRS and the University of Liège also financed some presentations in international conferences, as well as scientific stays in University Laval (Québec, Canada) and Newcastle University (Newcastle Upon Tyne, United Kingdom).

Thank you to the European Union FP6 Integrated Project AquaTerra (Project No. 505428), which allowed me to meet very interesting people and to have decisive discussions.

Many thanks to the Administration of the Walloon Region, the 'Compagnie Intercommunale Liégeoise des Eaux' (CILE), the 'Société Wallonne des Eaux' (SWDE), the 'Vlaamse Maatschappij voor Watervoorziening (VMW), and the 'Afdeling Waterbouwkundig Laboratorium' of the Flemish Region for all data they have provided about the Geer Basin.

Thank you to Christiane, Martine and Nadia, the secretariat staff, for their availability. Thank you to Annick Anceau, from the 'Earth Science Library' of the University of Liège, for helping me in bibliographic research. Thank you to the 'General IT department' (SEGI) of the University of Liège, for their precious advices about 'NIC3', the super computer for intensive calculation at University of Liège.

Particular thanks to all my colleagues and friends from the Hydrogeolgy Group and other teams:

Piotr, Philippe, Julie G., Ingrid, Samuel, Pierre J., Matthieu, Fabien, Julie C., Max, Pierre G., Nicolas, Cristina, Jordi, Laurent T., Jean -Michel, Tanguy, David, Jean, Frederic and many others.

Special thanks to Piotr who shared my office during more than 3 years.

Finally, I would like to address warm thanks to all my family. Particular thanks to you Céline for your support and comprehension during these years. Thank you for your patience when I was abroad. Thank you to have accompanied me in Quebec with the children. Thank you to be there with me.

Pascal Goderniaux (January 2010)

T ABLE OF CONTENTS

Summary __________________________________________________________________ 5 Acknowledgements __________________________________________________________ 7 Table of contents ___________________________________________________________ 9 List of figures _____________________________________________________________ 12 List of tables ______________________________________________________________ 15 Knowledge dissemination____________________________________________________ 16 1. Introduction __________________________________________________________ 21 1.1 Introduction ___________________________________________________________ 23 1.2 References _____________________________________________________________ 26 2. Scientific review _______________________________________________________ 27

2.1 Introduction ___________________________________________________________ 29 2.2 Groundwater modelling _________________________________________________ 29 2.3 Climate change modelling ________________________________________________ 34 2.3.1 Global Circulation Models (GCM) _______________________________________________ 35 2.3.2 Climate downscaling __________________________________________________________ 36 2.3.3 Greenhouse gases emissions scenarios ____________________________________________ 40 2.4 Evaluation of the uncertainty linked to climate change impact__________________ 42 2.5 References _____________________________________________________________ 44 3. Methodology __________________________________________________________ 49

3.1 Methodology ___________________________________________________________ 51 3.2 References _____________________________________________________________ 55 4. The Geer basin ________________________________________________________ 57

4.1 Geographical and geological contexts ______________________________________ 59 4.2 Hydrogeological context _________________________________________________ 60 4.3 References _____________________________________________________________ 64 5. Modelling ____________________________________________________________ 65

5.1 Hydrological modelling __________________________________________________ 67

5.1.1 Conceptual model ____________________________________________________________ 67

5.1.2 Mathematical and numerical model ______________________________________________ 67

5.1.3 Discretisation________________________________________________________________ 71

5.1.4 Specified Fluxes _____________________________________________________________ 72

5.1.5 Calibration procedure _________________________________________________________ 73

5.1.5.1 Parameter values ________________________________________________________ 74

5.1.5.2 Evaluation of the calibration _______________________________________________ 78

Table of contents

5.2 Sensitivity Analysis _____________________________________________________ 87 5.3 Discussion _____________________________________________________________ 91 5.4 Alternative calibration___________________________________________________ 94 5.5 References _____________________________________________________________ 97 6. Application of climate change scenarios on the Geer basin model _______________ 99

6.1 Climate scenarios ______________________________________________________ 101 6.2 Downscaling of RCM output_____________________________________________ 103 6.3 Projected changes in local climate ________________________________________ 104 6.4 Projected changes in hydrological regime __________________________________ 107 6.5 Discussion ____________________________________________________________ 110 6.6 References ____________________________________________________________ 119 7. Application of stochastic climate change scenarios on the Geer basin model _____ 121

7.1 Objectives ____________________________________________________________ 123 7.2 Simulation of the stochastic climate change scenarios ________________________ 124 7.2.1 Generation of precipitations times series using 'RainSim'_____________________________ 125 7.2.1.1 General concepts _______________________________________________________ 126 7.2.1.2 Calibration of the model for the control scenarios (without any climate change) ______ 127 7.2.1.3 Calibration of the model for the climate change scenarios _______________________ 129 7.2.1.4 Generation of the climate change time series for the Geer basin___________________ 134 7.2.2 Generation of PET time series using the 'CRU daily weather generator' _________________ 134 7.2.2.1 General concepts _______________________________________________________ 135 7.2.2.2 Generation of the climate change time series for the Geer basin___________________ 136 7.3 Application of the climate change scenarios on the Geer basin model ___________ 138 7.3.1 Simulation conditions ________________________________________________________ 138 7.3.2 Evolution and uncertainty of projected groundwater levels and surface water flow rates ____ 138 7.3.3 Temporal uncertainty of a specific event _________________________________________ 145 7.4 Verification simulations of modelling hypotheses ____________________________ 147 7.4.1 Influence of the number of equiprobable climatic scenarios___________________________ 147 7.4.2 Influence of the time discretisation ______________________________________________ 150 7.5 Discussion ____________________________________________________________ 153 7.6 References ____________________________________________________________ 156 8. Uncertainty linked to the calibration of the model and summary of the results ____ 157

8.1 Introduction and objectives______________________________________________ 159

8.2 Estimation of the uncertainty linked to the calibration of the hydrological model _ 160

8.2.1 Methodology _______________________________________________________________ 161

8.2.2 Application of the methodology on the Geer basin hydrological model __________________ 165

8.2.3 Discussions about the confidence intervals ________________________________________ 167

8.3.1 Uncertainty linked to the natural variability of the climate ____________________________ 170 8.3.2 Uncertainty linked to the climatic models_________________________________________ 171 8.3.3 Uncertainty linked to the downscaling technique ___________________________________ 171 8.3.4 Uncertainty linked to the calibration of the hydrological model ________________________ 172 8.4 Conclusions about uncertainty ___________________________________________ 174 8.5 References ____________________________________________________________ 177 9. Conclusions and perspectives ___________________________________________ 179

9.1 Conclusions___________________________________________________________ 181

9.1.1 Hydrological modelling_______________________________________________________ 181

9.1.2 Climate change scenarios _____________________________________________________ 182

9.1.3 Uncertainty evaluation _______________________________________________________ 183

9.1.4 Impact for the Geer basin _____________________________________________________ 184

9.1.5 General conclusion __________________________________________________________ 185

9.2 Perspectives __________________________________________________________ 185

Appendix ________________________________________________________________ 187

List of figures

L IST OF FIGURES

Figure 2.1. (Figure and legend from IPCC, 2001b). The time evolution of the globally average

temperature change relative to the control run of the CMIP2 simulations. […]...37

Figure 2.2. (Figure and legend from IPCC, 2000) Total global annual CO2 emissions from all sources (energy, industry, and land-use change) from 1990 to 2100 (in gigatonnes of carbon (GtC/yr)) for the families and six scenario groups. […] Each coloured emission band shows the range of harmonized and non-harmonized scenarios within each group. For each of the six scenario groups an illustrative scenario is provided (solid and dashed lines). […] ...42

Figure 4.1. Location of the Geer basin and hydrologic limits...59

Figure 4.2. Geological cross-section in the Hesbaye aquifer (modified from (modified from Brouyère et al., 2004b)), with a vertical exaggeration equal to 40...60

Figure 4.3. Piezometry of the chalk aquifer (in metres above sea level) based on 2088 data (modified from Orban, 2009; Ruthy, 2009). ...61

Figure 4.4. Evolution of groundwater levels at the observation well 'VIE044', from 1950 to 2006. ...63

Figure 5.1. Spatial discretisation of the Geer basin...72

Figure 5.2. Distribution of the hydraulic conductivity zones for the chalk finite elements layers (results of calibration)...75

Figure 5.3. (A) Computed steady-state surface water elevations. (B) Computed steady-state subsurface saturation, with full saturation shown in red (1967-1968)...79

Figure 5.4. Graphical analysis of the model calibration. (A) Computed values vs. observed values. (B) Residuals vs. observed values. (C) Weighted residuals vs. observed values. Doted lines represent increments of the calculated standard error. ...80

Figure 5.5 : Transient calibration of hydraulic heads for the nine observation wells...85

Figure 5.6. Transient calibration of surface flow rates for the Kanne gauging station (outlet) ...86

Figure 5.7. Composite Scales Sensitivities (CSS) of the parameters used in the Geer basin model ...89

Figure 5.8. (A) Aggregated sensitivities for each observation point. (B) Mean leverage statistics for each observation point. ...91

Figure 5.9. Transient calibration of hydraulic heads for the 8 observation wells (2nd model)...95

Figure 5.10. Transient calibration of surface flow rates for the Kanne gauging station (2nd model) ...95

Figure 5.11. Graphical analysis of the model calibration. (A) Computed values vs. observed values. (B) Residuals vs. observed values. (C) Weighted residuals vs. observed values...96

Figure 6.1. Monthly climatic changes for each climate change scenario (period 2071-2100) relatively to

1961-1990. (A) Temperature - Bierset climatic station. (B) Precipitation - Waremme

climatic station... 105

Figure 6.2. Monthly climatic changes for the three time period 2011-2040, 2041-2070, 2071-2100 (climate model RCAO_E) relatively to 1961-1990. (A) Temperature – Bierset climatic station. (B) Precipitation – Waremme climatic station... 106 Figure 6.3. Evolution of hydraulic heads at the eight observation wells for each climate change

scenario, using the model with calibration 1... 111 Figure 6.4. Evolution of hydraulic heads at the eight observation wells for each climate change

scenario, using the model with calibration 2... 112 Figure 6.5. Evolution of flow rates at gauging station 'Kanne' for each climate change scenario, using

the model with calibration 1... 113 Figure 6.6. Evolution of flow rates at gauging station 'Kanne' for each climate change scenario, using

the model with calibration 2... 113 Figure 7.1. Schematic of the NSRP stochastic rainfall model (Figure from Burton et al., 2008). The

circles represent the rainfall events. Each star is associated with the beginning of a 'rain cell'... 126 Figure 7.2. Observed, fitted and simulated precipitation statistics for the Geer basin climate

corresponding to the period 1961 – 1990... 129 Figure 7.3. Evolution of the scaling factors between 1975 and 2085 for GCM ECHAM4/OPYCA2... 131 Figure 7.4. Target climatic statistics of RCAO_E for years 1995, 2025, 2055, 2085 ... 132 Figure 7.5. Evolution of target and mean simulated statistics (for successive periods of 1000 years)

between 1975 and 2085 for RCAO_E ... 133 Figure 7.6. Stochastic climate change scenarios. Precipitations of RCAO_E and ARPEGE_H for

February and August ... 134 Figure 7.7. Stochastic climate change scenarios. Monthly mean temperature of RCAO_E and

ARPEGE_H for February and August... 137 Figure 7.8. Stochastic climate change scenarios. Monthly mean PET of RCAO_E and ARPEGE_H

for February and August... 137 Figure 7.9. Evolution of hydraulic heads at the 8 observation wells for 30 equiprobable climatic

scenarios of ARPEGE_H (2010 – 2085)... 140 Figure 7.10. Evolution of hydraulic heads at the 8 observation wells for 30 equiprobable climatic

scenarios of RCAO_E (2010 - 2085)... 141 Figure 7.11. Mean hydraulic heads at the 8 observation wells for each of the 6 climatic models (30

scenarios) ... 142 Figure 7.12. Evolution of water flow rate at the outlet of the basin for 30 equiprobable climatic

scenarios of ARPEGE_H and RCAO_E (2010 - 2085) ... 143 Figure 7.13. Mean water flow rate at the outlet of the basin for each of the 6 climatic models (30

scenarios) ... 143

List of figures

Figure 7.14. (A) Mean groundwater levels (30 scenarios) and 95% interval at observation point 'OTH002', (B) mean annual water flow rates (30 scenarios) and 95% interval at 'KANNE', for the control simulations and the climatic models ARPEGE_H and RCAO_E... 145 Figure 7.15. Number of outcomes (10 m decrease in OTH002 groundwater level) for each time

interval and each climatic model... 147 Figure 7.16. Probability density function and 95% interval for HIRHAM_H (OTH002)... 147 Figure 7.17. Results comparison when using 30 or 100 equiprobable scenarios of the climatic model

ARPEGE_H. (A) Mean groundwater levels and 95% interval at observation point 'OTH002'. (B) Mean annual water flow rates and 95% interval at 'KANNE'... 149 Figure 7.18. Comparisons of probability density functions and 95% intervals for ARPEGE_H

(OTH002) using 30 and 100 equiprobable scenarios. The vertical dotted lines represent the limits of the 95% interval... 149 Figure 7.19. Results comparison when using daily or monthly input solicitations for 30 equiprobable

scenarios of the climatic model ARPEGE_H. (A) Mean groundwater levels and 95%

interval at observation point 'OTH002'. (B) Mean monthly water flow rates and 95%

interval at 'KANNE' (February). (C) Mean monthly water flow rates and 95% interval at 'KANNE' (August). ... 152 Figure 8.1. Predictions and 95 % confidence interval around predicted values for 8 years of a

HIRHAM_H downscaled climate change scenario. (A) Absolute groundwater levels. (B) Groundwater levels change between a scenario without any climate change and the HIRHAM_H climate change scenario. ... 169 Figure 8.2. Predictions and 95 % confidence interval around predicted values for 8 years of a

HIRHAM_H downscaled climate change scenario. (A) Absolute surface water monthly flow rates. (B) Change in surface water monthly flow rates between a scenario without any climate change and the HIRHAM_H climate change scenario. ... 170 Figure 8.3. Summary of all results and uncertainties for the 8 groundwater observation points. The

horizontal red line represents the 'control' mean groundwater level without any climate change. ... 173 Figure 8.4. Summary of all results and uncertainties for the water flow rate at the outlet of the

catchment ('Kanne'). The horizontal line represents the 'control' mean flow rate without

any climate change. ... 174

L IST OF TABLES

Table 2.1. Selected examples of AOGCMs and spatial resolution (IPCC, 2001b; IPCC, 2007b)...36

Table 2.2. SRES emissions scenarios (IPCC, 2000)...41

Table 5.1. Parameters used in the flow model...68

Table 5.2. Van Genuchten parameters, total porosity and specific storage...74

Table 5.3. Full saturated hydraulic conductivities values of the calibrated zones (results of calibration)...75

Table 5.4. Values for the Manning roughness coefficients and coupling length ...76

Table 5.5. Root depths, evaporation depths and Leaf Area Index...77

Table 5.6. Mean errors between observed and computed heads for the nine observation wells (h

: observed hydraulic head, h

: computed hydraulic head, N: number of observations). ...82

Table 5.7. Simulated mean water balance terms for the period 1967-2003 ...87

Table 6.1. Climate change scenarios with corresponding RCM and GCM. DMI: Danish Meteorological Institute, HC: Hadley Center for Climate Prediction and Research, SMHI: Swedish Meteorological and Hydrological Institute... 102

Table 6.2. Variations of the mean water balance terms for each climate change scenario and time interval, using the model with calibration 1... 114

Table 6.3. Variations of the mean water balance terms for each climate change scenario and time interval, using the model with calibration 2... 115

Table 7.1. Random variables used in the NSRP model ... 127

Table 8.1. Summary of the results about climate change impact uncertainty for the period 2071-2100 (calculated with the first hydrological model, calibrated with daily stresses)... 170

Table A2. Van Genuchten parameters, total porosity and specific storage... 189

Table A3. Full saturated hydraulic conductivities values of the calibrated zones (results of calibration).. 189

Table A4. Values for the Manning roughness coefficients and coupling length ... 189

Table A5. Root depths, evaporation depths and Leaf Area Index... 190

Table A6. Simulated mean water balance terms for the period 1967-2003 ... 190

Knowledge dissemination

K NOWLEDGE DISSEMINATION

Journal articles

- Goderniaux P., Brouyère S., Fowler H.J., Blenkinsop S., Therrien R., Orban Ph., Dassargues A., 2009. Large scale surface - subsurface hydrological model to assess climate change impacts on groundwater reserves. Journal of Hydrology, 373, 1-2, 122-138 pp.

- Barth J.A.C., Kalbus E. , Schmidt C. , Bayer-Raich M., Reinstorf F., Schirmer M., Thiéry D., Dubus I.G., Gutierrez A., Baran N., Mouvet C., Petelet-Giraud E., Négrel Ph., Banton O., Batlle Aguilar J., Brouyère S., Goderniaux P., Orban Ph, Rozemeijer J.C., Visser A., Bierkens M.F.P., Van der Grift B., Broers H.P., Marsman A., Klaver G., Slobodnik J., Grathwohl P., 2007. Selected groundwater studies of EU project AquaTerra leading to large-scale basin considerations. Water Practice & Technology, 2-3, 10 pp.

- Visser A., Dubus I., Broers H.P., Brouyère S., Korcz M., Orban Ph., Goderniaux P., Batlle- Aguilar J., Surdyk N., Amraoui N., Job H., Pinault J.-L., Bierkens M., 2009. Comparison of methods for the detection and extrapolation of trends in groundwater quality. Journal of Environmental Monitoring, 11, 2030-2043 pp.

- Goderniaux et al., 2010. Climate change impact on groundwater reserves using stochastic climatic scenarios. Manuscript in preparation.

- Orban Ph., Brouyère S., Couturier J., Goderniaux P., Batlle-Aguilar J., Dassargues A., 2010.

Modelling nitrate trends a in chalk aquifer at regional scale. Manuscript in preparation.

- Blenkinsop S., Fowler H.J., Harpham C., Burton A., Goderniaux P., 2010. Modelling

transient climate change with a stochastic weather generator: Projected temperature changes

for the Geer catchment, Belgium. Manuscript in preparation.

Conference proceedings

- Goderniaux P., Brouyère S., Fowler H.J., Blenkinsop S., Therrien R., Orban Ph., Dassargues A., 2009. How can large scale surface - subsurface hydrological model be used to evaluate long term climate change impacts on groundwater reserves. The 7th International Conference on Calibration and Reliability in Groundwater Modeling. "Managing Groundwater and the Environment". Proceedings of MODELCARE 2009. Wuhan, China, September 2009, 137- 140 pp.

- Goderniaux P., Brouyère S., Dassargues A., 2007. Integrated approach for assessing climate change impacts on a regional chalky aquifer in Belgium. Changes in Water Resources Systems:

Methodologies to Maintain Water Security and Ensure Integrated Management. Proceedings of Symposium HS3006 at IUGG 2007, Perugia (Italy), July 2007, Vol. 315, 100-105 pp.

Conference abstracts

- Goderniaux P., Brouyère S., Fowler H.J., Blenkinsop S., Therrien R., Orban Ph., Dassargues A., 2009. Large Scale Integrated Surface - Subsurface Hydrological Model to Assess Climate Change Impacts on Groundwater Resources. The 2009 Ground Water Summit - The Science and Engineering Conference: Adapting to Increasing Demands in a Changing Climate.

National Ground Water Association. Tucson, USA, April 2009.

- Orban Ph., Brouyère S., Couturier J., Wildemeersch S., Goderniaux P., Batlle-Aguilar J., Dassargues A., 2009. Assessment of Nitrate Trends in Groundwater Using the Regional Scale HFEMC Approach. The 2009 Ground Water Summit - The Science and Engineering Conference: Adapting to Increasing Demands in a Changing Climate. National Ground Water Association. Tucson, USA, April 2009.

- Orban Ph., Goderniaux P., Batlle-Aguilar J., Brouyère S., 2009. Large-scale flow and transport modelling for the management of groundwater bodies. AquaTerra Final Conference.

Processes-Data-Models-Future Scenarios. Scientific Fundamental for River Basin Management. Tubingen, Germany, March 2009.

- Blenkinsop S., Fowler H.J., Burton A., Bovolo I., Van Vliet M., Goderniaux P., Forlin L.,

Harpham C., 2009. State-of-the-art climate change scenarios in AquaTerra. AquaTerra Final

Knowledge dissemination Conference. Processes-Data-Models-Future Scenarios. Scientific Fundamental for River Basin Management Tubingen, Germany, March 2009.

Scientific report – AquaTerra Project

- Goderniaux P., Brouyère S., Orban Ph., Dassargues A., Fowler H.J., Blenkinsop S., 2009.

Report on the development of the Geer hydrological model. Final results about climate change impacts evaluation. Deliverable R3.30, AquaTerra (Integrated Project FP6 no. 505428), 60 pp.

- Goderniaux P., Brouyère S., Orban Ph., Dassargues A., 2008. Development of the Geer basin hydrological model for climatic scenarios and first results about impacts evaluation.

Deliverable R3.26, AquaTerra (Integrated Project FP6 no. 505428), 29 pp.

- Goderniaux P., Brouyère S., Orban Ph., Dassargues A., 2007. Intermediate report on the development of the Geer hydrological model (surface and subsurface water) for climatic change scenario on that subcatchment. Deliverable R3.21, AquaTerra (Integrated Project FP6 no. 505428), 17 pp.

- Broers H.P., Visser A., Bierkens M., Dubus I., Pinault J.L., Surdyk N., Guyonnet D., Batlle- Aguilar J., Brouyère S., Goderniaux P., Orban Ph., Korcz M., Bronder J., Dlugosz J., Odrzywolek M., 2008. Draft overview paper on trend analysis in groundwater summarizing the main results of TREND2 in relation to the new Groundwater Directive. Deliverable T2.12, AquaTerra (Integrated Project FP6 no. 505428), 46 pp.

- Dubus, I., Pinault J.-L., Surdyk N., Guyonnet D., Broers H.P., Visser A., Orban Ph., Batlle- Aguilar J., Goderniaux P., Brouyère S., 2008. Report with comparison of statistical and physically deterministic methods of trend assessment and extrapolation in terms of data requirements, costs and accuracy. Deliverable T2.11, AquaTerra (Integrated Project FP6 no.

505428), 33 pp.

- Broers H.P., Visser A., Heerdink R., Van der Grift B., Surdyk N., Dubus I., Amaoui N., Orban Ph., Batlle-Aguilar J., Goderniaux P., Brouyère S., 2008. Report which describes the physically deterministic determination and extrapolation of time trends at selected test locations in Dutch part of the Meuse Basin, the Brévilles catchment and the Geer catchment.

Deliverable T2.10, AquaTerra (Integrated Project FP6 no. 505428), 47 pp.

- Hérivaux, C., Orban Ph., Batlle-Aguilar J., Brouyère S., Goderniaux P., 2008. Socio-economic analysis integrating soil-water system modelling for the Geer catchment (Meuse, Walloon region) - diffuse nitrate pollution in groundwater. Deliverable I3.8, AquaTerra (Integrated Project FP6 no. 505428), 45 pp.

- Orban, Ph., Batlle-Aguilar J., Goderniaux P., Dassargues A., Brouyère S., 2006. Description of hydrogeological conditions in the Geer sub-catchment and synthesis of available data for groundwater modelling. Deliverable R3.16, AquaTerra (Integrated Project FP6 no 505428), 20 pp.

Additional publications

- Brouyère S., Batlle-Aguilar J., Goderniaux P., Dassargues A., 2008. A New Tracer Technique for Monitoring Groundwater Fluxes: The Finite Volume Point Dilution Method. Journal of Contaminan Hydrology, 95, 3-4, 121-140 pp.

- Goderniaux P., Brouyère S., Gutierrez A., Baran N., 2010. Persistence of agricultural groundwater contamination related to hydraulic stratification as shown by long-term tracer tests. Submitted to Hydrogeology Journal. Under revision.

- Brouyère S., Batlle-Aguilar J., Goderniaux P., Dassargues A., 2007. A new single well tracer

test: The Finite Volume Point Dilution Method. Theory, field application and model

validation – in "Calibration and Reliability in Groundwater Modelling – Credibility in

Modelling – Pre-published proceedings of MODELCARE 2007. Copenhagen, Danemark, 67-

92 pp.

Knowledge dissemination

1. INTRODUCTION

1. Introduction

1.1 Introduction

According to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate

Change (IPCC, 2007a), "Warming of the climate system is unequivocal, as is now evident from

observations of increases in global average air and ocean temperatures, widespread melting of

snow and ice and rising global average sea level". This report also concludes with 'very high

confidence' that this global warming was mostly due to the effect of human activities through

emissions of greenhouse gases in the atmosphere. For more than one decade, issues linked to

climate change have actually raised in importance in societies and political circles. Though

international agreements are difficult to achieve, more and more nations have the reduction of

greenhouse gases emissions in their agenda. In 1997, several countries adopted the 'Kyoto

Protocol' which agrees on an average 5% reduction compared to 1990 emissions over the period

2008-2012 (UNFCCC, 2009). In 2009, the recent 'Copenhagen Accord' "[…] recognises the

scientific view that the increase in global temperature should be maintained below 2 degrees

Celsius […]" to stabilise greenhouse gas concentration in the atmosphere at a level that would

prevent dangerous anthropogenic interference with the climate system (UNFCCC, 2009). Climate

change is likely to bring harmful effects on many ecosystems and human life and activities. Rising

sea levels may submerge inhabited areas, inducing massive migration of population. The

progression of deserts or more frequent occurrences of violent and destructive climatic events

could make life very difficult in some areas, constraining people to adapt themselves to new

conditions. One of the most important indirect issues linked to climate change relates to water

supply, which is obviously essential for life. The availability of water is also necessary for almost

any human activities, including agriculture and associated food security issues. A lot of scientific

research about impact estimation has been carried out in this particular topic. Nevertheless, this

research is often restricted to surface water reserves, neglecting groundwater. However,

groundwater represents an important percentage of total water supply across the world.

1. Introduction

According to the United Nations Environment Program about groundwater (UNEP - Morris et

al., 2003), "the contribution from groundwater is vital". Perhaps as many as two billion people

depend directly upon aquifers for drinking water. Forty percent of the world's food is produced

by irrigated agriculture that relies largely on groundwater and over half of the largest megacities in

the world directly rely on local groundwater or make significant use of it. Groundwater often

constitutes the only available resource in arid zones, but is also largely used under more

temperate climates, like in Walloon Region, where approximately 80% of the water supply comes

from aquifers (DGARNE, 2009). Groundwater reserves will continue to be heavily used in the

future, because they constitute an important part of available freshwater in our planet, but also

because groundwater is less sensitive to sudden climatic variations, compared to surface water. In

2001, IPCC experts also reported that "Groundwater is the major source of water across the

world", but they also noticed that "There has been very little research on the potential effects of

climate change" (IPCC, 2001a). Considering this gap, the International Association of

Hydrogeologists (IAH) has created in 2004 a new commission on this particular topic to support

research, studies and international contacts. Though more research has been carried out since the

report of 2001, the lack of knowledge about groundwater and groundwater – surface water

interactions is repeated in the IPCC technical paper about "Climate Change and Water" in 2008

(Bates et al., 2008). Moreover, the few scientific works about climate change impact on

groundwater show variable results. Though differences in the studied climate and aquifer types

surely exert an influence, the way of representing climatic and hydrogeologic systems certainly

contributes to variability in results and uncertainty. Additionally, the uncertainty linked to climate

change impact is not evaluated, or from a very limited number possible uncertainty sources. The

evaluation of this uncertainty is however of major importance to give some credibility to the

climate change impact study. It also enables water managers to analyse risks and take decisions

with full knowledge of projected impact and their degree of confidence.

Considering this general context, the general objectives of this research are the following:

(1) development of a methodology for a reliable estimation of the climate change impacts on groundwater reserves;

(2) estimation of the uncertainties characterising these projected impacts, considering various possible uncertainty sources;

(3) pilot application of the two first objectives on the case of the Geer basin catchment (Belgium).

In Section 2 of this thesis, a review of the scientific studies performed in the field of groundwater and climate change is performed. In response to the relative weaknesses and strengths identified in this existing scientific literature, Section 3 presents the methodology developed and used in the current research. Section 4 describes the geological and hydrological contexts of the Geer basin.

Sections 5 to 8 expose the work performed and the main outcomes of the research, as obtained

following the methodology described in Section 3 ('Methodology'). Finally, Section 9 provides

conclusions and a reflection relative to the research perspectives.

1. Introduction

1.2 References

Bates, B.C., Kundzewicz, Z.W., Wu, S. and Palutikof, J., 2008. Climate Change and Water.

Technical Paper of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva.

DGARNE, 2009. Etat des nappes d'eau souterraine de la Wallonie. Huitième année. Décembre 2009., Service Public de Wallonie. Direction générale opérationnelle, Agriculture, Ressources naturelles et Environnement. Direction de l'état environnemental. Direction des eaux souterraines.

IPCC (Editor), 2001. Climate Change 2001: Impacts, Adaptation and Vulnerability, Contribution of the working group II to the third assessment report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge Univ. Press (UK), 1000 pp.

IPCC, 2007. Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC, Geneva, Switzerland.

Morris, B.L., Lawrence, A.R.L., Chilton, P.J.C., Adams, B., Calow, R.C. and Klinck, B.A., 2003.

Groundwater and its Susceptibility to Degradation: A Global Assessment of the Problem and Options for Management. Early Warning and Assessment Report Series, RS. 03-3.

United Nations Environment Programme, Nairoby, Kenya, 138 pp.

UNFCCC, 2009. http://unfccc.int - Official website of the United Nations Framework

Convention on Climate Change (UNFCCC).

2. SCIENTIFIC REVIEW

2. Scientific review

2.1 Introduction

A huge number of research studies have been performed about climate change, through various subjects such as the causes, modelling or impact of climate change on many types of systems.

The scientific review that follows this short introduction does not aimed to provide an inventory of all of them, but focuses on climate change impact studies performed in the field of groundwater, and on tools and methods used to achieve them. Particularly, it focuses on the groundwater modelling work performed in the context of climate change. It also presents the different modelling approaches to generate climate change scenarios and how they have been used in groundwater impact studies. Finally, issues relative to uncertainty evaluation are briefly discussed. This review attempts to highlight the weaknesses and strengths of previous studies, and constitutes a foreword to the research performed in the framework of this thesis.

2.2 Groundwater modelling

Estimating the possible impacts of climate change on water resources represents one of the most

difficult challenges faced by water managers. Because of the great interest in such projections,

several studies have been recently published on the topic (see for example Christensen et al.,

2004; Fowler et al., 2003; Fowler et al., 2007b; Gellens and Roulin, 1998; VanRheenen et al.,

2004; Wilby et al., 2006). As already mentioned in Section 1, most of these studies focus on

surface water and generally oversimplify or even neglect groundwater, although groundwater is

the main water supply in many parts of the world. Additionally, studies that try to assess climate

change impact on water resources are likely to produce variable results (Jiang et al., 2007). One of

the main reasons for the discrepancy in projections is that simplistic assumptions are often made

to represent the physical processes associated with hydrological systems. This is particularly the

case for the studies that account for groundwater, where the representation of processes

2. Scientific review

associated with subsurface flows and groundwater recharge brings additional complexity. These assumptions increase the uncertainty associated with model projections and need to be addressed.

A first requirement for estimating the impact of climate change on groundwater systems is a reliable estimate of the volume of water entering and leaving an aquifer. More specifically, a reliable estimate of groundwater recharge is needed because it represents the connection between atmospheric and surface-subsurface processes and is therefore a key element in the context of the impacts of climate change on groundwater. Similarly, in aquifers strongly influenced by surface water, groundwater discharge into rivers may be affected by changes in surface water levels, and consequently affect groundwater levels (Scibek et al., 2007). In previous studies about climate change impact on groundwater reserves (see for example Brouyère et al., 2004b; Chen et al., 2002; Holman, 2006; Loáiciga, 2003; Scibek et al., 2007; Serrat-Capdevila et al., 2007;

Woldeamlak et al., 2007), recharge has been estimated with various degrees of complexity, ranging from simple linear functions of precipitation and temperature (Chen et al., 2002; Serrat- Capdevila et al., 2007) to the application of "soil models" simulating variably-saturated groundwater flow and solute transport (Allen et al., 2004; Brouyère et al., 2004b; Scibek and Allen, 2006b).

Chen et al. (2002) and Chen et al. (2004) developed an empirical model, linking groundwater levels to climatic variables, for a carbonate rock aquifer in Manitoba (Canada), and proposed to use it for predicting groundwater levels under climate change conditions. Within this empirical model, recharge is linked to precipitation and temperature using simple water balance linear functions.

Serrat-Capdevilla et al. (2007) evaluate climate change impact on groundwater in the San Pedro

basin (Arizona, USA) for the period from 2000 to 2100, using a three dimensional 'MODFLOW'

model (Harbaugh, 2005). They calculate the 'mountain-front' recharge into the riparian basin using an empirical relationship linking recharge to precipitation.

Loaiciga et al. (2000) and Loaiciga (2003) use the 'GWSIM' (Ground Water Simulation Program) two-dimensional finite-difference model to study the impact of climate change on the Edwards regional karst aquifer (Texas, USA), under various pumping conditions. Recharge is estimated from a water balance of the streamflow in the area.

Allen et al. (2004), Scibek and Allen (2006b), Scibek and Allen (2006a), Scibek et al. (2007) study climate change impact on the 'Grand Forks' Aquifer (British Columbia, Canada) which is an alluvial aquifer strongly influence by river stages. They use a three dimensional 'MODFLOW' groundwater model, together with the more complex 'HELP' model (Hydrologic Evaluation of Landfill Performance – U.S. Environmental Protection Agency) (Schroeder et al., 1994) for recharge rates calculation, and the 'BRANCH' model (Schaffranek et al., 1981) for river stages calculation. The physically-based 'HELP' model enables to spatially calculate recharge rates taking into account processes such as runoff, infiltration, evapotranspiration, surface and moisture storage, snowmelt, etc.

Brouyère et al. (2004b) modelled groundwater flows under climate change conditions in a chalk aquifer in Belgium. The saturated groundwater flow model is implemented with the finite element code 'SUFT3D' (Brouyère, 2001; Carabin and Dassargues, 1999). Recharge rates are calculated with the soil model 'EPIC-GRID', which performs water budget at the ground surface level and in the unsaturated zone. Exchange fluxes are unidirectional, from the soil model to the groundwater model.

Woldeamlak et al. (2007) and Yusoff et al. (2002) performed similar studies for a part of the

Grote-Nete basin (Belgium) and a chalk aquifer (West Norfolk, UK), respectively. In both

studies, the authors developed a groundwater model using 'MODFLOW'. Woldeamlak et al.

2. Scientific review

(2007) calculate groundwater recharge rates with 'WetSpass' (Batelaan and De Smedt, 2001), a spatially-distributed water balance model. Yusoff et al. (2002) use a more conventional soil water balance method. Finally, other authors also studied the impact of climate change on groundwater recharge without assessing the effect on groundwater reserves (see for example Eckhardt and Ulbrich, 2003; Herrera-Pantoja and Hiscock, 2008; Holman, 2006; Holman et al., 2009; Jyrkama and Sykes, 2007).

Though groundwater recharge rates were estimated with various degrees of complexity in previous studies and models, none of them can simulate the feedback, or fluid exchange, between the surface and subsurface domains. This feedback is an integral component of the water cycle since groundwater recharge depends on precipitation and evapotranspiration at the surface domain, evapotranspiration in the vadose zone, evapotranspiration in the saturated zone when water levels are close to the ground surface, and finally river – aquifer interactions. The quantitative estimation of the latter four fluxes depends on the simulation of simultaneous hydraulic conditions in the surface and subsurface domains. Therefore, estimating recharge by only considering one part of the whole system is unrealistic, inaccurate and potentially unusable in the context of climate change impact assessments. Similarly, loosely coupled modelling approaches, where water exchange between surface and subsurface is calculated independently, do not provide a sufficient level of realism because they do not solve for all the interdependent processes simultaneously.

To compensate for this lack of interconnection when modelling groundwater, Van Roosmalen et

al. (2007) and Van Roosmalen et al. (2009) used a coupled model that simulates surface water and

groundwater flows simultaneously with water exchanges between both domains. This model,

described by Henriksen et al. (2003) and Sonneborg et al. (2003), and based on the 'MIKE SHE'

code (Graham and Butts, 2006; Refsgaard and Storm, 1995), is used to assess the impact of

relatively simple water balance method (Yan and Smith, 1994) to compute water flows in the

partially saturated zone. While this kind of simplification could be used in area where the

influence of the partially saturated zone is limited (provided some verifications), it constitutes a

serious limitation in other cases. The development and use of physically-based, fully-integrated

hydrological models able to simulate surface- and subsurface-flow in the saturated and partially

saturated zones, with a simultaneous solution of the flow equations in all domains, have recently

gained attention. Currently, the few modelling codes that are able to simulate these processes in

an integrated way include 'HydroGeoSphere' (Therrien et al., 2005), the 'Integrated Hydrology

Model' (InHM) (VanderKwaak, 1999), 'ParFlow' (Kollet and Maxwell, 2006), and 'OpenGeoSys'

(Delfs et al., 2009). As an example of application, Jones (2005) developed such a model for a 75

km² catchment (Laurel Creek Watershed – Ontario, Canada) using 'InHM'. The finite element

grid representing the catchment contained more than 600 000 nodes and transient simulations of

coupled surface and subsurface flow were run over periods of 1 month with specified fluxes

input on an hourly basis. Sudicky et al. (2008) present a similar model for a 17 km² subcatchment

of the Laurel Creek Watershed and use it to simulate contaminant transport issues. Another

example is reported by Li et al. (2008), who modelled, using HydroGeoSphere, surface and

subsurface flows, and evapotranspiration fluxes for a 286 km² catchment (Duffins Creek

Watershed – Ontario, Canada) with more than 700 000 nodes and made transient simulations

over 1 year periods with specified fluxes input on a daily basis. Finally, Kollet & Maxwell (2008)

used 'ParFlow' to implement an integrated surface – subsurface model for the Little Washita

watershed (Oklahoma, United States), which area is approximately 600 km², and performed 1-

year transient simulations with hourly time steps. A drawback of these models is that fully-

integrated simulations typically require substantial computer resources, and most simulations

published have been either limited to small catchments or short time periods. As an example, the

model of Jones et al. (2005) takes more than 4 days of computational time to simulate a period of

1 month with a 3.2 GHz Pentium4 desktop machine equipped with 4.0 Gb RAM. To our

2. Scientific review

knowledge, there is no example of such integrated surface – subsurface models used in the context of climate change impact evaluation, which requires to simulate longer time periods (typically 30 years minimum).

A second requirement for estimating the impact of climate change on groundwater systems is that hydrogeological system models must be capable of consistently representing observed phenomena, which is not always the case. As already presented above, Chen et al. (2002) propose to estimate the impact of climate change on a Canadian aquifer with an empirical model that links piezometric variations and groundwater recharge, where recharge is assumed to be a linear function of precipitation and temperature. Most studies focussing on surface water, such as Arnell (2003), also use simplistic transfer functions to represent exchanges between ground- and surface water. However, such transfer functions often oversimplify the exchange processes.

These functions can still be substituted for more detailed physical representations for specific conditions if they are verified with calibration, but their use may become uncertain if applied stresses go beyond the calibration conditions, which is typical for climate change scenarios.

Detailed physically-based and spatially-distributed models that take into account hydrogeologic processes provide more realistic simulations of groundwater fluxes, including exchanges with surface water.

2.3 Climate change modelling

In addition to the choice of the hydrological modelling approach, the need for climate change

scenarios, to be used as input of hydrological models, add an additional layer of complexity and

uncertainty to future projections. Before proceeding to any hydrological simulations to evaluate

potential impacts, climatic stresses are needed and specific scenarios have primarily to be

produced.

2.3.1 Global Circulation Models (GCM)

General Circulation Models (GCM) constitute an important tool to simulate climatic variables.

According to Grotch and MacCracken (1991), General Circulation Models are "numerical models that attempt to simulate the global climate by calculating the evolution of the atmosphere in all three spatial dimensions based on the conservation laws for atmospheric mass, momentum, total energy, and water vapour". GCMs are actually complex numerical climate models based on physical laws and implemented at the scale of the earth globe. A GCM is generally the combination of an 'Atmospheric General Circulation Model' (AGCM) and an 'Oceanic Circulation Model' (OGCM) to form an 'Atmosphere-Ocean General Circulation Model' (AOGCM). According to their complexity and continuous developments, AOGCMs are also coupled with other models such as sea-ice models, land surface processes, biosphere processes, carbon cycle models, atmospheric chemistry, etc. (IPCC, 2001b; IPCC, 2007b). Numerous GCMs are available and have been developed during the last decade. Some of them are shown in Table 2.1 along with specific characteristics such as spatial resolution. GCMs can then be used to generate climate change scenarios by increasing, for example, the emissions of CO

or other greenhouse gases. As an illustration, Figure 2.1 (IPCC, 2001b) shows the evolution of simulated global temperature using different GCMs and considering a CO

increase of 1% per year.

However, a drawback linked to the use of GCMs is that they are very computationally expensive.

Additionally, GCMs simulate climatic variables at a very large scale, typically for spatial

resolutions between 2° and 5° of latitude and longitude. Hydrological or hydrogeological studies

are on the other hand performed at a much more local scale, typically from a few hundred

squared metres to several squared kilometres. GCMs are unable to accurately simulate climatic

variables for this smaller scale because they do not account for local effects such as topography,

hydrography, land use, etc. As a result, a comparison between climatic observations at a specific

measurement station and the climatic outputs from a particular GCM would probably lead to

2. Scientific review

significant differences. Considering these issues, and particularly the mismatch of scales between the spatial resolution of GCM outputs and the need for more local hydrological impact studies, additional work is needed to produce appropriate climatic models and scenarios in this context.

Numerous techniques have been developed for years and are known as 'downscaling' techniques.

All existing techniques have been reviewed in several scientific papers, including Fowler et al.

(2007a) and Wilby and Wigley (1997). A very brief overview of these techniques, based on these review papers is presented here after.

AOGCM Institute Resolution

(atmosph.) Resolution

(ocean) References CCSM3 National Centre for Atmospheric

Reasearch, USA 1.4° × 1.4° 1.0° × 1.0° (Collins et al., 2004) CGCM3.1 Canadian Centre for Climate

Modelling and Analysis, Canada 1.9° × 1.9° 10.9° × 1.4° (McFarlane et al., 1992)

CNRM-CM3 Météo-France, France 1.9° × 1.9° 2.0° × 2.0° (Deque et al., 1994) ECHAM4/OP

YC3 Max Planck Institute for Meteorology,

Germany 2.8° × 2.8° 2.8° × 2.8° (Roeckner et al., 1996)

ECHAM5/MP

I-OM Max Planck Institute for Meteorology,

Germany 1.9° × 1.9° 1.5° × 1.5° (Roeckner et al., 2003)

UKMO-

HadCM3 Hadley Centre for Climate Prediction

and Research/MET Office, UK 2.5° × 3.75° 1.25° × 1.25° (Gordon et al., 2000;

Pope et al., 2000) UKMO-

HadGEM1 Hadley Centre for Climate Prediction

and Research/MET Office, UK 1.3° × 1.9° 1.0° × 1.0° (Martin et al., 2004) Table 2.1. Selected examples of AOGCMs and spatial resolution (IPCC, 2001b; IPCC, 2007b).

2.3.2 Climate downscaling

Downscaling techniques can be classified into two main categories: 'dynamical downscaling' and

'statistical downscaling'.

Figure 2.1. (Figure and legend from IPCC, 2001b). The time evolution of the globally average temperature change relative to the control run of the CMIP2 simulations. […]

'Dynamical downscaling' mainly relates to the use of physically-based Regional Climate Models (RCM). RCMs are climate models with a higher spatial resolution than GCMs. The modelled area is limited in space and time-dependant boundary conditions are provided and driven by GCMs.

According to Fowler et al. (2007a), RCMs typically provide climatic simulations at a ~0.5°

latitude and longitude scale. First climate applications using RCMs were performed by Dickinson

et al. (1989) and Giorgi (1990). The complexity and number of climate processes included in

RCMs have then progressively increased. Compared to GCMs, RCMs can take into account

some additional regional features such as, for example, topography, vegetation cover, presence of

lakes, etc., which improves the performance of RCMs in reproducing regional climate and

extreme events. The European FP5 PRUDENCE project (Prediction of Regional scenarios and

Uncertainties for defining European Climate change risks and Effect) (Christensen et al., 2007)

produced such kind of high spatial resolution simulations for an ensemble of RCMs, using

2. Scientific review

different GCMs boundary conditions and greenhouse gases emission scenarios (see Section 2.3.3). However, RCMs also suffer from various drawbacks. As GCMs, Regional Climate Models are computationally expensive and simulations are usually limited in simulated time. In the context of climate change, simulations are typically performed only for the time slices 1961-1990, representing a climate without any change, and 2070-2100, representative of a stationary climate change over the 30-years period of the end of the century. Another major drawback is link to the fact that RCMs results are strongly dependent on the driving boundary conditions selected from GCMs. Though more accurate than GCMs, the use of Regional Climate Models is usually not sufficient to produce climatic scenarios for local hydrological studies and further statistical downscaling is generally required.

'Statistical downscaling' is based on empirical or statistical relationships betweens GCMs or RCMs outputs and the local climate to be simulated. Typically, relationships are defined between a GCM or RCM 'control simulation' without any climate change and observed climatic data from a local measurement station. To generate climate change scenarios, the same relationships are then used but with the corresponding GCM or RCM outputs that consider a specific climate change. Statistical downscaling methods allow providing climatic scenarios at the very local scale needed for most hydrological studies. They also have the advantage of being computationally inexpensive and quite flexible. Nevertheless, they can only be applied where observed climatic data are available in sufficient quantities. According to Fowler et al. (2007a) and IPCC (2001b;

2007b), there exist a huge range of different statistical downscaling methods, from very simple to

highly complex models involving large numbers of variables and parameters. They allow

modelling local climate with various degrees of complexity. Fowler et al. (2007a) classify statistical

downscaling methods into three main categories: the 'regression models', the 'weather typing

schemes' and the 'weather generators' techniques. Briefly, the 'regression' or 'transfer function'

methods use direct relationships between GCM or RCM variables (the 'predictors') and local

climate variables (the 'predictants') through the use of regression methods, for example. The 'weather typing scheme' relates to the occurrence of particular 'weather classes' to local climate (Fowler et al., 2007a). Various methods exist for defining these 'weather classes', specifically for the downscaling purposes. The 'weather generators' (Wilks and Wilby, 1999) are statistical models that enable to generate climate scenarios based on the statistical distributions of a series of climatic variables. Each distribution is determined using observed local climatic data and can be modified based on GCMs or RCMs outputs. The 'weather generators' have the great advantage that they enable to easily generate large numbers of stochastic climatic scenarios, that can be used in subsequent probabilistic analyses of climatic variables or any potential impact.

To date, studies examining the impacts of climate change on groundwater systems have adopted

relatively simple statistical downscaling methods. One of the most straightforward approaches is

the 'perturbation' or 'delta change' method (Prudhomme et al., 2002) which applies 'change

factors' (CFs), calculated as difference between the control and future GCM simulations, to

observations (e.g. Brouyère et al., 2004b; Yusoff et al., 2002). However, since these scenarios

were produced by applying the projected changes to mean temperature and precipitation to the

whole of the corresponding future distribution, they fail to reflect changes in the shape of the

distribution, which is important for extremes or changes in the distribution of wet and dry

periods. In their impact study concerning groundwater in the Grand Forks aquifer, Scibek and

Allen (2006b) use the 'LARS-WG' weather generator (Semenov et al., 1998) to generate a single

100-years scenario representing a stationary climate for each of the periods 2010-2039, 2040-2069

and 2070-2099. (Holman et al., 2009) use the 'CRU' weather generator (Kilsby et al., 2007; Watts

et al., 2004) to produce 100 realisations of 30-years scenarios for the period 2040-2069 and use

them as input of a one-dimensional soil water balance to calculate groundwater recharge.

2. Scientific review 2.3.3 Greenhouse gases emissions scenarios

As briefly explained above, a large panel of climate models is available to produce climatic simulations in relation with objectives and needs of specific studies. More particularly, they are widely used to generate climate change scenarios for climate change impact studies, based on CO

and other greenhouse gases emissions, among others. The Intergovernmental Panel on Climate Change (IPCC) developed a set of emissions scenarios known as the SRES scenarios (Special Report on Emission Scenarios) (IPCC, 2000). All scenarios are regrouped within 4 different storylines which describe the context of the emission scenarios in various domains such as economy, technology, demography, etc. These four storylines are described in details by IPCC (2000). Main characteristics are summed up in Table 2.2. Figure 2.2 (IPCC, 2000) shows the range of projected CO

emissions between 1990 and 2100 for each of the four storylines. The sets of greenhouse gases emission scenarios, available for each storyline, can then be used in GCMs to produce climate change scenarios for specific climatic variables. As an illustration, the different existing GCMs used with the A2 emission scenarios project a global temperature increase between 1.3°C and 4.5°C for the period 2070-2100 relatively to the period 1961-1990.

The global temperature increase for the B2 emission scenarios varies between 0.9°C and 3.4°C.

Generally, the increase is expected to be higher for Northern Europe (IPCC, 2000; IPCC,

2001b).

Storyline A1

- World that becomes more homogeneous with increased social and cultural interactions - Very rapid economic growth

- Global population that peaks in mid-century and declines after - Rapid introduction of new and more efficient technologies

Scenario A1 is divided into 3 sub-categories based on their energy sources: fossil intensive (A1F1), non-fossil energy sources (A1T), balance across all sources (A1B)

Storyline A2

- Very heterogeneous world with self-reliance and preservation of local identities - Regionally oriented economic development

- Global population that continuously increases

- More fragmented and slower technological change than other scenarios Storyline B1

- World that becomes more homogeneous

- Rapid change in economic structures toward a service and information economy, with reductions in material intensity

- Global population that peaks in mid-century and declines after - Introduction of clean and resource-efficient technologies Storyline B2

- Heterogeneous world

- Emphasis is on local solutions to economic, social and environmental sustainability - Global population that continuously increases, but at a lower rate than scenario A2 - Less rapid and more diverse technological change than in the scenarios A1 and B1

Table 2.2. SRES emissions scenarios (IPCC, 2000)