Increasing detail of distributed runoff modeling using fuzzy logic in curve number

详细信息    查看全文

• 作者：Runkui Li (1)
  Xiaoping Rui (1)
  A-Xing Zhu (2) (3) (4)
  Junzhi Liu (2)
  Lawrence E. Band (3) (5)
  Xianfeng Song (1)
  
  1. College of Resources and Environment ; University of Chinese Academy of Sciences ; Beijing ; 100049 ; China
  2. School of Geography ; Nanjing Normal University ; Nanjing ; 210023 ; China
  3. State Key Lab of Resources and Environmental Information System ; Institute of Geographical Sciences and Natural Resources Research ; Chinese Academy of Sciences ; Beijing ; 100101 ; China
  4. Department of Geography ; University of Wisconsin-Madison ; 550 North Park Street ; Madison ; WI ; 53706 ; USA
  5. Department of Geography ; University of North Carolina at Chapel Hill ; Chapel Hill ; NC ; 27599 ; USA
  
• 关键词：Runoff curve number ; Hydrologic soil group ; Fuzzy logic ; Soil and water assessment tool (SWAT) ; Distributed hydrological modeling
• 刊名：Environmental Earth Sciences
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：73
• 期：7
• 页码：3197-3205
• 全文大小：1,122 KB
• 参考文献：1. Burrough, PA (1989) Fuzzy mathematical methods for soil survey and land evaluation. Eur J Soil Sci 40: pp. 477-492 CrossRef
  2. Elhakeem, M, Papanicolaou, A (2009) Estimation of the runoff curve number via direct rainfall simulator measurements in the state of Iowa, USA. Water Resour Manag 23: pp. 2455-2473 CrossRef
  3. Hjelmfelt AT, Woodward DA, Conaway G, Quan QD, Mullem JV, Hawkins RH (2001) Curve Numbers, Recent Developments. In: Proceedings of the 29th IAHR Congress, Beijing, China. pp 285鈥?91
  4. Li, RK, Zhu, AX, Song, XF, Li, BL, Pei, T, Qin, CZ (2012) Effects of spatial aggregation of soil spatial information on watershed hydrological modelling. Hydrol Process 26: pp. 1390-1404 CrossRef
  5. Mark, DM, Csillag, F (1989) The nature of boundaries on 鈥榓rea-class鈥?maps. Cartographica 26: pp. 65-78 CrossRef
  6. McCuen, RH (2004) Hydrologic analysis and design. Prentice Hall, Upper Saddle River
  7. Mishra, S, Sahu, R, Eldho, T, Jain, M (2006) An improved ia-s relation incorporating antecedent moisture in SCS-CN methodology. Water Resour Manag 20: pp. 643-660 CrossRef
  8. Mishra, S, Jain, M, Suresh Babu, P, Venugopal, K, Kaliappan, S (2008) Comparison of AMC-dependent CN-conversion formulae. Water Resour Manag 22: pp. 1409-1420 CrossRef
  9. Musgrave GW (1955) How much of the rain enters the soil? US Department of Agriculture, Yearbook of Agriculture: Water. US Govt. Printing Office, Washington, DC, pp 151鈥?59
  10. Nielson RD, Hjelmfelt AT (1998) Hydrologic soil-group assignment. In: Abt SR, Young-Pezeshk J, Watson CC (eds) Water Resource Engineering 98. In: Proceedings of the International Water Resources Engineering Conference. American Society of Civil Engineers, Reston, Virginia, pp 1297鈥?302
  11. Ponce, VM, Hawkins, RH (1996) Runoff curve number: has it reached maturity?. J Hydrol Eng 1: pp. 11-19 CrossRef
  12. Schaap, MG, Leij, FJ, Genuchten, MT (2001) ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. J Hydrol 251: pp. 163-176 CrossRef
  13. Shirmohammadi, A, Yoon, KS, Rawls, WJ, Smith, OH (1997) Evaluation of curve number procedures to predict runoff in GLEAMS. J Am Water Resour Assoc 33: pp. 1069-1076 CrossRef
  14. Soil Conservation Service (1972) National Engineering Handbook. Section聽4: Hydrology. Soil Conservation Service, Washington, DC
  Soil survey manual. Soil Conservation Service, Washington
  15. USDA (2004) National Engineering Handbook, 210-VI. Part 630, chapter 9, Hydrologic Soil-Cover Complexes. Natural Resources Conservation Service, Washington, DC
  16. USDA (2007) National Engineering Handbook, title 210-VI. Part 630, chapter 7, Hydrologic Soil Groups. Natural Resources Conservation Service, Washington, DC
  17. Mullem, JA (1991) Runoff and peak discharges using green-ampt infiltration model. J Hydrol Eng 117: pp. 354-370 CrossRef
  18. Van Mullem JA, Woodward DE, Hawkins RH, Hjelmfelt AT Jr (2002) Runoff curve number method: beyond the handbook. Second Federal Interagency Hydrologic Modeling Conference, Las Vegas, Nevada
  19. Ye, XC, Zhang, Q, Viney, NR (2011) The effect of soil data resolution on hydrological processes modelling in a large humid watershed. Hydrol Process 25: pp. 130-140 CrossRef
  20. Young, DF, Carleton, JN (2006) Implementation of a probabilistic curve number method in the PRZM runoff model. Environ Model Softw 21: pp. 1172-1179 oft.2005.06.004" target="_blank" title="It opens in new window">CrossRef
  21. Zhai, H, Benson, C (2006) The log-normal distribution for hydraulic conductivity of compacted clays: two or three parameters?. Geotech Geol Eng 24: pp. 1149-1162 CrossRef
  22. Zhu, AX (1997) A similarity model for representing soil spatial information. Geoderma 77: pp. 217-242 CrossRef
  23. Zhu, AX, Band, L, Vertessy, R, Dutton, B (1997) Derivation of soil properties using a soil land inference model (SoLIM). Soil Sci Soc Am J 61: pp. 523-533 CrossRef
  24. Zhu, AX, Hudson, B, Burt, JE, Lubich, K, Simonson, D (2001) Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Sci Soc Am J 65: pp. 1463-1472 CrossRef
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：None Assigned
• 出版者：Springer Berlin Heidelberg
• ISSN：1866-6299

文摘

The Soil Conservation Service Curve Number runoff model is widely used in runoff prediction and has been incorporated into many software packages for watershed modeling. The Curve Number (CN) is the key parameter in the model, but it is largely dependent on Hydrologic Soil Group (HSG) classifications which may induce aggregation of detailed soil information. However, little attention and efforts have been paid to reduce such aggregation effect for retaining those valuable soil information to derive more detailed CN. This study proposed to integrate fuzzy logic to derive detailed CN. Membership of a given soil to each HSG is first calculated based on soil properties and HSG classification criteria; then, detailed and continuous CN is derived using the membership as weight for CN of each soil-cover complex. The proposed approach was incorporated into an automation system and its further effects on runoff modeling were examined. A case study shows fuzzy CN possesses more spatial details and leads to obvious spatial differences of simulated runoff. The developed system could also be used to detect inconsistency of HSG placements.