SWAT 187 2007 Jain et al. 2010 4 936. 02 km 2 Onstad & Jamieson 1970 Faith et al. 2009 Li et al. 2010 1 7 9 / 2009 SWAT Srinivasan & Arnold 1994 Saleh

Chinese Journal of Ecology 2013 32 1 186-194 SWAT * 1 2 5 2 3** 4 ( 1 中 国 科 学 院 地 理 科 学 与 资 源 研 究 所, 北 京 100101; 与 环 境 保 育 重 点 实 验 室, 临 沂 师 范 学 院, 山 东 临 沂 276005; 院 大 学, 北 京 100049) 2 山 东 师 范 大 学 人 口 资 源 与 环 境 学 院, 济 南 250014; 4 中 国 科 学 院 烟 台 海 岸 带 研 究 所, 山 东 烟 台 264003; 3 山 东 省 水 土 保 持 5 中 国 科 学 3S SWAT 1987 2017 96% 4 2. 61 0. 38 0. 34 mm km - 2-0. 11 mm km - 2 SWAT CA-Markov X522 A 1000-4890 2013 1-0186 - 09 Runoff response to land use change in Baimahe basin of China based on SWAT model. WANG Xue 1 2 5 ZHANG Zu-lu 2 3** NING Ji-cai 4 1 Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China 2 School of Population Resources and Environment Shandong Normal University Jinan 250014 China 3 Shandong Key Laboratory of Soil and Water Conservation and Environmental Protection / Linyi University Linyi 276005 Shandang China 4 Yantai Institute of Coastal zone Reseavchi Chinese Academy of Sciences Yantai 264003 Shandong China 5 Lniversity of Chinese Academy of Sciences Beijing 100049 China. Chinese Journal of Ecology 2013 32 1 186-194. Abstract By using 3S technology and based on the analysis and prediction of the land use change in Baimahe basin a SWAT model was established to study the runoff response of the basin under the scenarios of different land use. In the meantime the contribution coefficients of the main land use types in the basin to the runoff depth were calculated. From 1987 to 2017 the main land use types in the basin were farmland construction land forestland and shrub land occupying 96% or more of the total land area while the grassland waters and unused land only had a smaller proportion. The four main land use types had different effects on the runoff depth. The contribution coefficient of forestland shrub land and construction land to the runoff depth was 2. 61 0. 38 and 0. 34 mm km - 2 respectively implying that these three land use types had positive effects on the runoff depth in this basin. On the contrary the contribution coefficient of farmland was - 0. 11 mm km - 2 implying that farmland had negative effect on the runoff depth. Key words SWAT model CA-Markov model land use runoff response Baimahe basin. * STKF201002 2009ZX07210-007-01 ** E-mail zulzhang@ 126. com 2012-08-06 2012-10-19

SWAT 187 2007 Jain et al. 2010 4 936. 02 km 2 Onstad & Jamieson 1970 Faith et al. 2009 Li et al. 2010 1 7 9 / 2009 SWAT Srinivasan & Arnold 1994 Saleh et al. 2000 Nosetto et al. 2005 1 20 70 1. 1 1. 1. 1 DEM 2004 USDA 1994 SWAT http / /srtm. csi. cgiar. org Soil and Water Assessment Tool 90m 1. 1. 2 1987 1997 2007 TM 3 2 1 Fig. 1 Location of Baimahe basin

188 32 1 2 Fig. 2 Land use patterns of the study area in different years Kappa 0. 84 0. 81 0. 79 SWAT 1 1. 1. 3 DBF 1 500000 3 2004 1970 2000 19 1. 2 Matlab 3 SWAT 1. 2. 1 CA-Markov CA-Markov Markov CA 2010 Markov SPAW CA 2003 2007 Markov CA Liu & Andersson 2004 3 Fig. 3 Soil type map of the study area 1. 1. 4 CA 4 2011 30 m 30 m 7 7 IDRISI Andes 15. 0 MCE CA-Markov 5 5

SWAT 189 5 5 10 1. 2. 3 2017 2012 2010 1. 2. 2 SWAT SWAT R x V a V b V c V d km 2 A x F x W x U x 2012 mm km - 2 x 1987 1997 2007 2017 R x / 8 R x = V a A x + V b F x + V c W x + V d U x + 2 N P n m p q m p q x / 1987 1997 2007 2017 Kannan et al. 2007 3 SWAT SW t = SW 0 + t R day - Q surf - E a - W deep - Q gw R m - R n i =1 R p - R A m - A n F m - F n W m - W n U m - U n V a 1 m A p - A m F p - F m W p - W m U p - U m V R q - R p = b SW t mm SW 0 i A q - A p F q - F p W q - W p U q - U p V c R mm i R day i q - R m A q - A m F q - F m W q - W m U q - Um Vd mm Q surf i mm E a i mm W 3 deep i mm Q gw i V a V b V c V d mm DEM 2 HRUs 2. 1 2. 1. 1 1987 2007 2007 HRUs 2007 SWAT 73 386 HRUs 7 1987 1997 2007 1 1 1987 2007 Table 1 Land use changes from 1987 to 2007 in Baimahe basin 1987 1997 2007 1987 2007 km 2 a - 1 km 2 % km 2 % km 2 % km 2 723. 77 77. 32 704. 35 75. 24 657. 54 70. 24-66. 23-6. 02 64. 60 6. 90 55. 25 5. 90 44. 31 4. 73-20. 29-1. 85 35. 83 3. 83 33. 79 3. 61 42. 81 4. 57 6. 99 0. 64 10. 35 1. 11 1. 83 0. 20 2. 45 0. 26-7. 90-0. 72 83. 72 8. 94 124. 34 13. 28 170. 48 18. 21 86. 77 7. 89 16. 58 1. 77 15. 33 1. 64 16. 75 1. 79 0. 16 0. 02 1. 24 0. 13 1. 19 0. 13 1. 75 0. 19 0. 51 0. 05

190 32 1 1987 2007 2017 1 4 2 96% 1987 96. 99% 1997 2 2017 98. 04% 2007 97. 76% 2% 2007 > > > > > > 1987 2007 2007 2017 66. 23 1997 2007 20. 29 7. 90 km 2 50. 85 km 2 0. 53 km 2 2010 9 56. 92 2. 85 km 2 2. 2 SWAT - 6. 02 km 2 a - 1-0. 72 km 2 a SWAT - 1 1987 1997 1997 2007 Li et al. 26. 68% 1987 2007 2010 SWAT 6. 986 km 2 86. 77 km 2 7. 89 km 2 a Beven Binley 1992 GLUE Generalized - 1 Likelihood Uncertainty Estimation SWAT Beven & Binley 1992 GLUE SWAT SCE 0. 51 km 2 Shuffled Complex Evolution GLUE 0. 05 km 2 a - 1 3 2. 1. 2 CA-Markov CA-Markov 2 2017 1987 1997 Table 2 Statistics of land use types in 2017 in Baimahe basin IDRISI Andes 15. 0 Markov 1987 1997 CA-Markov 2007 Kappa 0. 867 CA-Markov 2007 Markov 1997 2007 2007 2017 2007 2017 km 2 % 600. 63 64. 16-56. 92-5. 17 41. 46 4. 43-2. 85-0. 26 48. 12 5. 14 5. 31 0. 48 3. 25 0. 35 0. 80 0. 07 221. 34 23. 64 50. 85 4. 62 19. 07 2. 04 2. 33 0. 21 2. 29 0. 24 0. 54 0. 05

SWAT 191 3 1991 1995 Table 3 Parameters sensitive to runoff and their value GLUE ranges ALPHA_BF SOL_K 1 a 0. 01 0. 07-0. 20 0. 80 SFTMP - 0. 30 1. 00 CN2 Ⅱ SCS - 0. 07 0. 90 CH_K2 5. 60 12. 00 SOL_AWC 1 0. 02 0. 40 ALPHA_BNK 0. 00 1. 00 CH_N2 0. 00 0. 08 GW_DELAY 34. 00 43. 00 SOL_BD 1-0. 05 0. 06 SWAT Nash- Sutcliffe NS R 2 2004 NS n i = 1 Q o i - Q m i 2 NS = 1-4 n Q o i - 珚 Q 0 2 i = 1 n Q o i - 珚 Q o Q m i - 珚 Q m 2 R 2 i = 1 = 5 n Q o i - 珚 Q o 2 n Q m i - 珚 Q m 2 i = 1 i = 1 Q o i Q m i 珚 Q o NS > 0. 5 R 2 > 0. 6 1991 1995 1996 2000 4 5 4 4 5 R 2 SWAT 4 0. 9 0. 7 ~ 0. 9 0. 5 ~ 0. 69 < 0. 5 4 Kannan 2007 SWAT 1970 2000 1986 1995 4 1991 1995 Fig. 4 Comparison of simulated and observed yearly runoff data during calibration 1991-1991 1995 1996 2000 1995 5 Fig. 5 Comparison of stimulated and observed monthly data during calibration and validation

192 32 1 4 SWAT Table 4 Calibation and validation results of SWAT model NS R 2 0. 77 0. 79 0. 74 0. 75 NS R 2 134. 15 mm 15. 8% 2017 0. 7 SWAT 1987 1997 2007 2. 3 1986 2000 2. 3. 1 1987 1987 2017 8. 65% 1997 2007 2017 8. 21% 8. 00% 8. 96% SWAT 1986 2000 6 6 5 1986 1991 1987 2017 R 2 0. 7 1992 2000 1987 2017 R 2 4 96% 1987 2007 1986 2000 129. 10 119. 98 115. 86 mm 2007 2017 1986 2000 115. 86 mm 2. 3. 2 6 Fig. 6 Annual runoff depth under different land use scenarios 5 Table 5 Changes of annual runoff depth under different land use scenarios 1987 1997 2007 2017 1986 1991 y = 7. 24x + 98. 49 y = 6. 95x + 90. 53 y = 6. 86x + 86. 69 y = 7. 35x + 103. 08 R 2 0. 76 0. 75 0. 75 0. 77 1992 2000 y = - 1. 55x + 140. 35 y = - 1. 55 + 131. 14 y = - 1. 52x + 126. 90 y = 1. 51x + 145. 28 R 2 0. 09 0. 09 0. 09 0. 09

SWAT 193 6 1987 1997 Table 6 Coefficients of different land use types on runoff depth 1997 2007 V Δ 1987-1997 Δ 1997-2007 Δ 2007-2017 - 0. 11 2. 14 5. 15 6. 26 2017 2. 61-24. 41-28. 56-7. 44 0. 38-0. 77 3. 43 2. 02 0. 34 13. 81 15. 69 17. 29 Δ 1987-1997 Δ 1997-2007 Δ 2007-2017 1987 1997 1997 2007 2007 2017 mm 0. 34 mm km - 2 2% 3 4-0. 11 MATLAB mm km - 2 4 V a V b V c V d - 0. 11 2. 61 0. 38 0. 34 mm km - 2 6 4 6 > > > 2. 61 mm km - 2 3 20 80 SWAT 2. 61 0. 38 0. 34 mm km - 2 2010-0. 11 mm km - 2 1987 2007 2001 2017 0. 38 mm km - 2 1989 1989 2004

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