Climate Change Impacts on River Floods: Uncertainty and Adaptation. Lu Wang

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1 Climate Change Impacts on River Floods: Uncertainty and Adaptation Lu Wang

3 Climate Change Impacts on River Floods: Uncertainty and Adaption Proefschrift ter verkrijging van de graad van doctor aan de Technische Universiteit Delft, op gezag van de Rector Magnificus prof. ir. K.C.A.M. Luyben, voorzitter van het College voor Promoties, in het openbaar te verdedigen op donderdag 30 April 2015 om uur door Lu Wang Master of Science in Hydrology and Water Resources Engineering Hohai University, China geboren te Handan, China.

4 This dissertation has been approved by the promotors: Prof. drs. ir. J.K. Vrijling and Prof. dr. ir. P.H.A.J.M. van Gelder Composition of the doctoral committee: Rector Magnificus Prof. drs. ir. J.K. Vrijling Prof. dr. ir. P.H.A.J.M. van Gelder promotor promotor Independent members: Prof. dr. ir. S.N. Jonkman Prof. dr. W. Wang Prof. dr. R. Ranasinghe Dr. S. Maskey Dr. R. Jongejan Substitute member: Prof. dr. ir. M.J.F. Stive CiTG, TU Delft Hohai University, China UNESCO IHE/The Australian National University UNESCO IHE Jongejan RMC CiTG, TU Delft This work was financially supported by the China Scholarship Council (CSC) This work was partly supported by Stichting Het Lamminga Fonds, Delft, the Netherlands Published by: VSSD, Delft, the Netherlands ISBN Copyright 2015 by Lu WANG All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without the prior permission on the author. Author

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45 Calibration strategy Methods & Abbreviation Transformation for precipitation Transformation for temperature Reference of existing methods Mean based method M1: MB-BC Schmidli et al. (2006) ; Lenderink et al. (2007) Biascorrection strategy Variance base method M2: VB-BC Quantile mapping M3: QM-BC Quantile correcting M4: QC-BC The same with precipitation The same with precipitation Bouwer and Aerts (2004); Ho et al. (2012); Hawkins et al. (2013); (Wood 2002, Wood et al. 2004); Ines and Hansen (2006); Deque (2007); Themeßl et al. (2011); Mpelasoka and Chiew (2009); Li et al. (2010); Hemer et al. (2012) Transfer function M5: TF-BC (Piani et al. 2009, Piani et al. 2010); Zhang (2005) Mean based method M6: MB-CF Prudhomme et al. (2002); Fowler et al. (2007) Changefactor strategy Variance base method M7: VB-CF Quantile mapping M8: QM-CF Quantile correcting M9: QC-CF The same with precipitation Shabalova et al. (2003); Hawkins et al. (2013) The same with precipitation Räisänen and Räty (2012) Mpelasoka and Chiew (2009) Transfer function M10: TF-CF Räisänen and Räty (2012)

49 (a) (b) Figure 3.3 Performance ranking of empirical downscaling methods used for downscaling daily temperature (a) and daily precipitation intensity (b). BC and CF calibration strategies are indicated by black dashed lines and red solid lines, respectively.

58 (a) (b) Figure 3.10 Changes at percentiles in downscaled future daily temperature (a) and daily precipitation intensity (b) at Station 7. BC and CF calibration strategies are indicated by black dashed lines and red solid lines, respectively.

61 (a) (b) Figure 3.13 ECDFs of downscaled future daily precipitation at Station 7 (left) and areal mean at one GCM grid (right) for selected seasons

96 1 Source Choice of period for calibrating hydrological model (CP) Group Size Combination of Each group Sample Size of 3 12(GCM)*3(ES)*6(DM)*100(EP) Equifinal parameter sets (EP) (GCM)*3(ES)*6(DM)*3(CP) Downscaling method (DM) 6 12(GCM)*3(ES)*3(CP)*100(EP) Emission scenario (ES) 3 12(GCM)*6(DM)*3(CP)*100(EP) GCM 12 3(ES)*6(DM)*3(CP)*100(EP) 5400

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107 C = I 0 +I 1 X + p S n i=1 i 1 r + 1 Min[μ(C) + k σ (C)] σ

108 p = 1 e 1+C H A B 1 C σ E(Y ) = E(E(Y P)) = μ(p) D(Y ) = D P [E(Y P)] + E P [D(Y P)] = μ(p) [1 μ(p)] i 1 1+ r = 1 i =1 r

109 1 1 = i=1 1+ r (1+ r) 2 1 ( ) 2 i μ(c) = I 0 + I 1 X + μ(p) S r σ 2 (C) = 2 μ(p) [1 μ(p)] S (1+ r) 2 1 σ Var(Y S) = Var(Y ) Var(S)+ E 2 (Y ) Var(S)+ Var(Y ) E 2 (S) = μ(p) (1 μ(p)) σ 2 (S)+ μ 2 (P) σ 2 (S)+ μ(p) (1 μ(p)) μ 2 (S) = μ(p) σ 2 (S)+ (1 μ(p)) μ 2 (S) μ(c) = μ(i 0 ) + μ(i 1 ) X + μ(p) μ(s) r σ 2 (C) = σ 2 (I 0 ) + σ 2 (I 1 ) X 2 + μ(p) [σ 2 (S) + (1 μ(p)) μ 2 (S)] (1+ r) 2 1 σ σ σ

110 μ(p) = 1 e 1+C H A ( B ) C f (C)f (B)f (A)dC db da, H > A Climit = 0 Climit A H μ(p) = 1 e 1+C H A ( B ) C Climit f (C)f (B)f (A)dC db da, H < A Climit = 0 A H μ(p) = 1 e 1+C H A ( B ) C f (B)f (A)dB da, 0 C = 0

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112 R = R ratio Future, p present, p p ratio R future... GCMs, p p = Rbaseline GCMs, p

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115 Present Climate Future Climate Baseline period Now Future period 2 GCMs (2GCMs 3 scenarios)

116 μ(p)

117 μ(p)

118 μ(c) = I 0 + I 1 X + P 0 μ(s) r σ 2 (C) = P 0 σ 2 (S)+ ( 1 P 0 ) μ 2 (S) (1+ r) 2 1 μ(c) = I 0 + I 1 X + μ(p) μ(s) r σ 2 (C) = μ(p) (1 μ(p)) μ2 (S) (1+ r) 2 1 μ(p) σ 2 (S)+ ( 1 μ(p) ) μ 2 (S) σ 2 (C) = (1+ r) 2 1

119 σ μ(c)+ k σ(c) μ(c) σ(c) μ(c)σ(c) μ(c)+ k σ(c)

120

121 P i = P 0 e γ i γ = ln μ(p) ln P 0 m

122 μ(p) μ(c) = E(I total )+ E(R before2070,pattern2 )+ E(R after2070 ) 56 1 = I 0 + I 1 X + S P i 1 + r + μ(p) S i=1 i 187 i= r i σ 2 (C) = Var(R before2070,pattern2 )+ Var(R after2070 ) = S P i (1 P i ) (1+ r) 2 + μ(p) ( 1 μ(p) ) S 2 i=1 i 186 i=57 1 (1+ r) 2 i

123 μ(c) = E(I total )+ E(R before2070,pattern3 )+ E(R after2070 ) = I 0 + I 1 X + P 0 S 56 i=1 i 1 1+ r + μ(p) S i= r i σ 2 (C) = Var(R before2070,pattern3 )+ Var(R after2070 ) = P 0 (1 P 0 ) S 2 56 i=1 i 1 (1+ r) 2 + μ(p) ( 1 μ(p) ) S 2 i=57 1 (1+ r) 2 i

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