Ps22Pdf

25 6 Vol. 25 No. 6 2010 6 JOURNAL OF NATURAL RESOURCES Jun., 2010 DEM TOPMODEL 1, 2, 2, 3 ( 1., 510275; 2., 430072; 3., 100044) : 3, 1: 5 DEM, TOPMODEL,,,, DEM,, DEM,, 200 m : ; DEM ; TOPMODEL : P333. 9 : A : 1000-3037( 2010) 06-1022 - 11,, ( Digital Elevation Model, DEM), DEM [ l], DEM [ 1],, DEM, DEM,, DEM,, ; S rensen Seibert DEM [ 2 ] ; DEM, TOPMODEL [ 3 ] DEM,, [ 4-5 ],, Beven Binley 1992 GLUE ( General Likelihood Uncertainty Estimation),, [ 6-9 ], Blasone Vrugt [ 6 ], [ 7-8 ], : 2009-10- 19 ; : 2010-03- 25 : ( 50809078 50839005) ; ( 2008 B043) ; ( IWHR02009003 ) ; : ( 1980- @ mail. sysu. edu. cn ),,,,, E-mail: linkr

6 : DEM TOPMODEL 1023,,, ;,,,,, DEM, DEM, 3,, TOPMODEL, DEM,,, 1,,, 1 570 km, 15. 9 10 4 km 2,, ;,,, ;,,,, 1 224 km 2,,,,,,, 6 448 km 2,, 2 000 3 000 m,, 100 130 km,,, 3 219 km 2,, 1 5 ( 20 m) DEM, 25 m 50 m 100 m 200 m 400 m 800 m DEM 1 3 DEM 1 : DEM,, / / ;,,, DEM, 100 m 2 2. 1 TOPMODEL( TOPography based hydrological MODEL), Beven Kirkby 1979, [ 10-11],,,

1024 25 1 DEM Table 1 Major topographic variables of different DEM resolutions DEM DEM 25 m 50 m 100 m 200 m 400 m 800 m / % 44. 90 40. 50 33. 61 26. 27 19. 68 14. 51 //( km 2 /km 2 ) 1. 133 7 1. 120 4 1. 098 4 1. 071 6 1. 046 6 1. 029 0 //( km 3 /km 2 ) 0. 593 6 0. 592 3 0. 590 1 0. 585 4 0. 577 8 0. 568 8 2. 39 6. 35 13. 86 25. 15 42. 03 67. 60 0. 010 0. 018 0. 030 0. 046 0. 069 0. 103 / % 64. 72 59. 96 51. 34 40. 18 29. 04 19. 69 //( km 2 /km 2 ) 1. 224 6 1. 195 4 1. 152 8 1. 101 3 1. 056 2 1. 026 7 //( km 3 /km 2 ) 0. 975 9 0. 975 6 0. 974 7 0. 971 8 0. 964 0 0. 934 5 3. 02 8. 23 19. 62 39. 16 66. 86 102. 93 0. 013 0. 023 0. 041 0. 066 0. 098 0. 135 / % 53. 20 47. 56 38. 63 29. 09 20. 99 14. 42 //( km 2 /km 2 ) 1. 1674 1. 148 9 1. 116 8 1. 078 1 1. 045 7 1. 024 5 //( km 3 /km 2 ) 0. 7246 0. 723 8 0. 722 6 0. 719 6 0. 715 4 0. 705 6 2. 92 7. 89 17. 32 30. 81 48. 93 74. 89 0. 013 0. 023 0. 039 0. 060 0. 089 0. 126 TOPMODEL,,, TOPMODEL ( ) ln( / tan ),, tan,,, - 2. 2 TOPMODEL 4 S zm T 0 T d SR max S zm ; T 0 ; T d ; SR max,, 2 2 Table 2 Ranges of parameters used in TOPMODEL model S zm m 0. 01 1 0. 5 T 0 m 2 h - 1 0. 01 3 1. 5 T d h 1 100 50 SR max m 0. 01 0. 5 0. 25

6 : DEM TOPMODEL 1025 1 TOPMODEL, E a, P 0, P 1, EX i, S rz, EX s, S uz, SD, q v, Q v, A i, Q b 1 TOPMODEL [ 7] Fig. 1 Flow of TOPMODEL 3 3. 1, DEM,, ; 2 3 3 DEM 2 Fig. 2 DEM Flow path length against DEM resolutions

1026 25 2 3, DEM, 400 m, DEM 3 Table 3 Flow path length of different DEM resolutions DEM 25 m 50 m 100 m 200 m 400 m 800 m /km 47. 4 46. 7 46. 15 43. 01 39. 56 37. 78 /km 85. 21 84. 42 83. 77 78. 51 80. 19 69. 41 /km 104. 76 103. 93 101. 13 93. 82 84. 43 82. 88 /km 184. 32 182. 98 176. 71 163. 02 145. 22 143. 12 /km 75. 89 75 73. 07 69. 04 51. 59 45. 73 /km 181. 19 179. 1 171. 3 156. 71 123. 44 112. 35 3. 2 DEM,, DEM 3 4 3 DEM, 3 4 : DEM ;, DEM,, ;,, DEM ; DEM, 3 DEM Fig. 3 Topographical index distribution against DEM resolutions

6 : DEM TOPMODEL 1027 4 DEM Table 4 Characteristic variables of topographical index of different DEM resolutions 25 m 50 m 100 m 200 m 400 m 800 m 5. 06 5. 74 6. 53 7. 05 7. 78 8. 38 1. 33 1. 51 1. 59 2. 08 2. 3 2. 36 C v 0. 263 0. 262 0. 244 0. 296 0. 296 0. 28 C s 1. 57 1. 63 1. 54-0. 285-0. 279-0. 322 4. 97 5. 67 6. 36 7. 06 7. 93 8. 81 1. 48 1. 71 1. 73 1. 83 1. 9 2. 01 C v 0. 298 0. 302 0. 271 0. 259 0. 24 0. 228 C s 1. 67 1. 84 1. 76 0. 76 0. 48 0. 15 5. 06 5. 69 6. 45 7. 05 7. 79 8. 58 1. 53 1. 68 1. 74 1. 93 2 2. 23 C v 0. 303 0. 296 0. 269 0. 273 0. 257 0. 26 C s 1. 63 1. 62 1. 55 0. 23-0. 01-0. 15 3. 3,, Nash-Sutcliffe,,,,,, CRIW IS, [ 8] 3 1980 1990, 4 018; DEM, GLUE, Nash-Sutcliffe, 70%, 95% ; CRIW IS, 4 5 4 5 : Fig. 4 4 DEM Plot of evaluation index of uncertainty against DEM resolutions

1028 25 5 DEM Table 5 Assess index of uncertainty of different DEM resolutions 25 m 50 m 100 m 200 m 400 m 800 m CR IW IS 0. 908 0. 906 0. 907 0. 912 0. 914 0. 910 38. 109 37. 105 38. 039 40. 087 40. 948 39. 822 1. 760 1. 480 1. 567 1. 410 1. 600 1. 643 CR IW 0. 257 0. 315 0. 248 0. 726 0. 717 0. 374 0. 269 0. 598 0. 454 0. 711 0. 495 0. 277 0. 932 0. 930 0. 931 0. 941 0. 939 0. 931 84. 052 82. 946 83. 373 89. 577 90. 227 86. 810 IS 2. 283 2. 017 2. 200 1. 815 1. 870 2. 052 CR IW 0. 242 0. 286 0. 255 0. 796 0. 707 0. 101 0. 295 0. 515 0. 368 0. 714 0. 61 0. 235 0. 933 0. 930 0. 931 0. 941 0. 941 0. 935 41. 847 41. 448 42. 253 44. 760 45. 140 43. 559 IS 2. 308 2. 017 2. 161 1. 815 2. 000 2. 074 0. 298 0. 288 0. 187 0. 777 0. 735 0. 369 0. 336 0. 523 0. 315 0. 704 0. 56 0. 388 DEM, DEM, ; DEM 200 m, CR IW ; DEM 200 m, ; DEM IS, DEM 200 m,, [ 12], [ 13 ],,,,, ;,, 6,,, 6, DEM,, DEM, 5 6 :, ; DEM 3, 200 m ;,, DEM,,,, DEM,, [ 1] ; DEM,,

6 : DEM TOPMODEL 1029,,,, 6 Table 6 Weight of evaluation index CR 1 0. 473 0 IW 0. 667 0. 315-1 IS 0. 448 0. 212-1 CR 1 1 /3 0 IW 1 1 /3-1 IS 1 1 /3-1 :, 0, - 1,,,,,,,,,,,,,,, 200 m 5 200 m ; 6 Fig. 5 5 Scattergram of likelihood value against the model parameter

1030 25 6 200 m 95% ( 1983 ) Fig. 6 Comparison of prediction limits in Xixia basin when DEM resolution is 200 m 200 m 95% 5 0. 941,, 95%, 6 TOPMODEL,, 4 ( 1), DEM,, / / ;,,, DEM, 100 m ( 2) DEM DEM, ;, ; ( 3) CR IW IS,, DEM, DEM,, 200 m, DEM TOPMODEL,,,,, ( References) : [ 1],,,. DEM [ J]., 2003, 58( 6) : 824-830. [ TANG Guo-an, ZHAO Mu-dan, LI Tian-wen, et al. Modeling slope uncertainty derived from DEMs in Loess Plateau. Acta Geographica Sinica, 2003, 58( 6) : 824-830. ] [ 2] S rensen R, Seibert J. Effects of DEM resolution on the calculation of topographical indices: TWI and its components [ J]. Journal of Hydrology, 2007, 347: 79-89. [ 3],,,. DEM [ J]., 2007, 33( 12 ) : 12-14. [ LIN Kai-rong, LIU Shan-shan, CHEN Hua, et al. Effects and study of digital elevation model grid scale on hydrological model-

6 : DEM TOPMODEL 1031 ing. Water Power, 2007, 33( 12) : 12-14. ] [ 4],,,. [ J]., 2004, 15( 4) : 495-500. [ LIU Chang-ming, XIA Jun, GUO Sheng-lian, et al. Advances in distributed hydrological modeling in the Yellow River basin. Advance in Water Science, 2004, 15( 4 ) : 495-500. ] [ 5],. [ J]., 2002, 13( 1) : 93-104. [ YE Shou-ze, XIA Jun. Century s retrospect and looking into the future of hydrological science. Advance in Water Science, 2002, 13 ( 1) : 93-104. ] [ 6] Blasone R S, Vrugt J A. Generalized likelihood uncertainty estimation ( GLUE) using adaptive Markov Chain Mote Carlo sampling [ J]. Advances in Water Resources, 2008, 31: 630-648. [ 7] Xiong L H, O Connor K M. An empirical method to improve the prediction limits of the GLUE methodology in rainfall-runoff modeling [ J]. Journal of Hydrology, 2008, 349: 115-124. [ 8],,,. - [ J]., 2009, 40( 4) : 464-473. [ WEI Xiao-jing, XIONG Li-hua, WAN Min, et al. Application of Markov Chain Monte Carlo method based modified generalized likelihood uncertainty estimation to hydrological models. Journal of Hydraulic Engineering, 2009, 40( 4) : 464-473. ] [ 9],,. Copula-Glue [ J]. :, 2009, 48( 3 ) : 109-115. [ LIN Kai-rong, CHEN Xiao-hong, JIANG Tao. Study of parameter uncertainty in hydrological model based on copula and glue. Acta Scientiarium Naturalium Universitatis Sunyatsen, 2009, 48( 3) : 109-115. ] [ 10],. [ M]. :, 2004. [ XIONG Li-hua, GUO Shenglian. The Distributed Watershed Hydrological Model. Beij ing: China Waterpower Press, 2004. ] [ 11],,,. DEM [ J],, 2008, 39 ( 11) : 18-20. [ LIN Kai-rong, GUO Sheng-lian, CHEN Hua, et al. Distributed for Hydrological process simulation in Hanzhong basin by DEM-based Distributed Hydrological Model. Yangtze River, 2008, 39( 11) : 18-20. ] [ 12]. [ M]. :, 1990. [ CHEN Shou-yu. Fuzzy Hydrology and Fuzzy Optimize Theory of Water Resources System. Dalian: Dalian Technology University Press, 1990. ] [ 13],, [ J]., 2006, ( 4) : 48-51. [ LUO Zheng, LIN Kai-rong. Application of fuzzy optimization on assessing hydrological simulation. China Rural Water and Hydropower, 2006, ( 4 ) : 48-51. ]

1032 25 The Impact of DEM Resolution on TOPMODEL Simulation Uncertainty LIN Kai-rong 1, GUO Sheng-lian 2, XIONG Li-hua 2, NIU Cun-wen 3 ( 1. School of Geographical Science and Planning, Sun Yat-sen University, Guangzhou 510275, China; 2. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China; 3. China Institute of Water Resources and Hydropower Research, Beijing 100044, China) Abstract: The hydrological uncertainty is one of the most important aspects in hydrological science research. This paper focuses on how resolution impacts the hydrological uncertainty using the TOPMODEL model based on the topographic index, which extracts from DEMs of different resolutions in the Hanjiang River. Three geomorphologic areas are selected as test areas, representing different terrain types from smooth to rough. Their DEMs are produced from digitizing contours of 1 50000 scale topographical maps. Hydrological uncertainty was assessed synthetically by using the multiple-objectives fuzzy optimal method. It is found from the analysis on different spatial resolutions that the topographic characteristics parameters of river basin are affected by DEM resolution remarkably, so as to affect the hydrological uncertainty, but this effect is not very great due to the hydrological complexity, and 200 m should be the more suitable grid size for hydrological uncertainty in this area. The integrated method of assessing hydrological uncertainty is a new throughway to study the various hydrological uncertainties. Key words: hydrology and water resources; DEM resolution; TOPMODEL