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1 GEO GRA P HICAL RESEA RCH Vol127, No12 Mar1, ,5, 1,6, 3, 2, 3, 4 (11, ; 21, ; 31, ; 41, ; 51, ; 61, ) : GL U E,, :, UM, (R 2 ), CS, R 2, B, R 2, : GL U E ; ; ; ; ; : (2008) [1 ] : 1), ; 2), ; 3),,,,, ( Genetic Algorit hm, GA) [2,3 ] (Simulated Annerling,SA) [4 ] (Artificial Neural Network,ANN) [5,6 ],,, Beven (1992) ( GL U E, Gen2 eralized Likelihood Uncertainty Estimation), [7 ] Beven (2001) [ 8 ] Tawhai ( 01038km 2 ), : ; : : ( , ) ; ( CCSF ) ; : (19832),,, E2MAIL : 3 : (19652),,,, E2MAIL :

2 GL U E TOPMOD EL, GL U E (2004) [9 ] GL U E,, GL U E,, [ 10 ] GL U E (2004, 2006), CO2, GL U E [11 ], L ISFLOOD2WB 8 [12 ] ; Gallart (2006) [13 ] Top model,,, ; (2006) [14 ] GL U E, GL U E, [10,15 17 ] [ ],, [ ], GL U E,,,, [20 ] Fig11 Xinanjiang model flow chart

3 2 : 345 : 1) ; 2) : ; 3) ; 4) 4 : :, KC UM L M C ; :, WM B IMP ; :, SM EX KG KI ; :, CI CG CS KE XE KE XE,,, 1 ; 1 [20 ] 1 Tab11 Parameters and their sampled ranges of Xinanjiang model GL U E UM (mm) L M (mm) DM (mm) WM (mm) WM = UM + L M + DM B EX KC C SM (mm) KG KI + KG = < KI + KG < 1 KI CG CI CS 0-1 IMP GL UE,, ( Equifinality), GL U E, [7 ], [ 9 ] ( 1),, Nash R 2, GL U E R 2 = 1 - n c i = 1 n c ( Qi - ^Qi) 2 i = 1 ( Qi - gqc) 2 (1) (1) : R 2 ; Qi ; ^Qi ; gqc ;

4 nc 3 ( , ),,,,, ( , ),,,,,,, 34 %, ,, 2 -, [18 ], 2 2 Tab12 Basic hydrological data of two basins (mm) (km 2 ) ( (a) (b) ) Fig12 Relationship between annual rainfall and runoff of (a) Jiuzhou and (b) L ushi 4 411, 3 E = N R2 > m N Total (2) : E ; N R 2 > m R 2 > m ; N Total GL U E ; m R 2 3, R 2 ( R 2 = 015 ) ;, (2)

5 2 : 347,, R 2 3 Fig13 Comparisons of simulation efficiency in two basins 412,, GL U E,, [7 ], 3 3 Tab13 Several equif inality parameter groups UM ( mm) L M ( mm) DM ( mm) B EX KC C SM ( mm) KG KI CG CI CS IMP R

6 348 27, : X ij = X ij m X ij i = 1 (3) : X ij ; X ij ( i, ), m (3) j 4 ( R 2 ), L M, CS, 4 ( (a) ; (b) ) Fig14 Total standardization of equifinality parameter group s in (a) Jiuzhou and (b) L ushi 413, ( 5 7), :,, ( 5) UM L M DM EX CG, R 2, 5 (UM) ( (a) ; (b) ) Fig15 Scatter plots of the likelihood of UM for daily discharge in (a) Jiuzhou and (b) L ushi :,, ( 6) KI + KG KC CS KI ( ), KI + KG ( 6 (a), (b) ), KI + KG [ 20 ],

7 2 : 349

8 350 27,, KC ( ) CS ( 6 (c), (d) ),, R 2, : ( 7) B C IMP KG CI WM SM B ( ), C ( 7 (b) ), IMP ( ), KG 1 ( ), WM (WM = UM + L M + DM, ) ( 7 (f) ) CI 1 ( 7 (c) ), SM ( ),,,,,, 5 GL U E : 1) 2) GL U E ( ),, GL U E 3), : UM L M DM EX CG,, R 2 ; KI KI + KG KC CS,, R 2, ; C IMP WM B SM KG CI,,,,, : 1),,,,, 2),, 3),,, 4), GL U E, 5),, [12 ],

9 2 : 351 : : [ 1 ] Sivapalan M K, Takeuchi S W, Franks S W1 IA HS decade of prediction in ungauged basins ( PUB), : Shaping an exiting future for t he hydrological sciences1 Hydrological Sciences Journal,2003,48 (6) : [ 2 ], 1 1,2006,26 (4) : [ 3 ],, 1 1,2004, (2) : [ 4 ], 1 1,2001,20 (1) : [ 5 ],, 1 1,1998,17 (4) : [ 6 ], 1 1,1999,18 (4) : [ 7 ] Beven K, Binley A M1 The fut ure of distributed models : Model calibration and uncertainty prediction1 Hydrologi2 cal Processes,1992,6 (3) : [ 8 ] Keit h Beven, Jim Free1 Equifinality,data assimilation, and uncertainty estimation in mechanistic modeling of com2 plex environmental systems using t he GL U E met hodology1 Journal of Hydrology,2001,249 : [ 9 ], 1 GL U E 1 : 1 1 :, [ 10 ],,, 1 1,2007,22 (4) : [ 11 ] Mo Xingguo, Beven K1 Multi2objective parameter conditioning of a t hree2source wheat canopy model1 Agricultural and Forest Meteorology, 2004,122 : [ 12 ] MO Xingguo, et al1 Parameter conditioning and prediction uncertainties of t he L ISFLOOD2WB distributed hydro2 logical model1 Hydrological Sciences2Journal2des Sciences Hydrologiques,2006,51 (1), [13 ] Gallart F,et al1 Using internal catchment information to reduce t he uncertainty of discharge and baseflow predic2 tions1 Advances in Water Resources,2006 :1 161 [ 14 ] Zhang Danrong, Beven K, Mermoud A1 A comparison of non2linear least square and GL U E for model calibration and uncertainty estimation for pesticide t ransport in soils1 Advances in Water Resources, 2006, 29 ( 12) : [ 15 ] Free J, Beven K J,Ambroise B1 Bayesian estimation of uncertainty in runoff prediction and t he value of data : An application of t he GL U E approach1 Water Resources Research,1996,32 (7) : [ 16 ] Keit h B, Zak S1 Equifinality, sensitivity and uncertainty in t he estimation of critical load1 Science of t he Total En2 viron ment,1999,236 : [ 17 ] Romanowicz R J,Beven K J1 Comment s on generalized likelihood uncertainty estimation1 Reliability Engineering and System Safety,2006,91 : [ 18 ] 1 1 :, [ 19 ] 1 1 :, [ 20 ], 1 1,1988, 6 :2 91 [ 21 ] 1 1 :, [ 22 ], 1 ( ) 1,1989,17 (4) : [ 23 ], 1 1,2006,28 (4) : [ 24 ], 1 GL U E 1,2006,24 (259) : [ 25 ], 1 1 : 1 1 :,

10 Uncertainty analysis of Xinanjiang model parameter SHU Chang 1,5, L IU Su2xia 1,6, 3, MO Xing2guo 2, L IAN G Zhong2min 3, DA I Dong 4 (11 Key Lab of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research ( IGSNRR), CAS, Beijing , China ; 21 Key Lab of Ecological Net Observation and Modeling, IGSNRR, CAS, Beijing , China ; 31 State Key Laboratory of Hydrology2Water Resources and Hydraulic Engineering, Hohai University, Nanjing ,China ; 41Bureau of Hydrology, Yellow River Conservancy Commission, Zhengzhou , China ; 51 Graduate School of the Chinese Academy of Sciences,Beijing , China ; 61 National Meteorological Center, China Meteorological Administration, Beijing , China) Abstract :The uncertaint y p ro blem in hydrological model is an impo rtant issue of scientific research at p resent, which cover s t hree aspect s of data, model st ruct ure and parame2 ters1 Parameter is one of t he key roles in analyzing model uncertainty problem1 The value of parameters depends on characteristics of a basin, but in fact it is difficult to obtain be2 cause t here are few o bservatio n statio ns1 In general, it needs to co nfirm parameter s by several calibratio n met hods including Genetic Algo rit hm, Simulated Annerling and Artifi2 cial Neural Network1 So t here exist s parameter uncertainty problem1 The generalized like2 lihood uncertainty estimation ( GL U E) met hodology is an effective approach to st udy un2 certainty of parameters1 In this paper, t he uncertainty in Xinanjiang model is examined by employing GL U E1 Based on t he simulation result s of daily data f rom Jiuzhou ( ) and L ushi ( ) basins, it is found t hat t he p henomenon of equifinality exist s among parameters group s for bot h of t he basins1 According to comparison result of scatter plot s, parameters of Xinanjiang model can be classified into t hree group s : sensitivity pa2 rameters such as UM, EX ; non2sensitivity parameters such as KC, CS and regional sensi2 tivity parameters such as B, WM1 The conclusion is favorable for understanding parame2 ter s of Xinanjiang model so as to p rovide valuable scientific informatio n for simulating hydrological processes1 Finally it p ut s forward t he main content s on f ut ure uncertainties research in hydrological modeling1 Key words :GL U E met hodology ; Xinanjiang model ; equifinality ; uncertainty ;L ushi basin ; Jiuzhou basin

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