Development of lag time and time of concentration for a tropical complex catchment under the influence of long-term land use/land cover (LULC) changes

  • Jabir Haruna AbdulkareemEmail author
  • Biswajeet PradhanEmail author
  • Wan Nor Azmin Sulaiman
  • Nor Rohaizah Jamil
GCEC 2017
Part of the following topical collections:
  1. Global Sustainability through Geosciences and Civil Engineering


Lag time (tL) and time of concentration (Tc) are essential time parameters used in hydrological flood-flow design methods for estimating peak discharge and flood hydrograph shape. They form the basis of a number of hydrological models used among the scientists. Kelantan River basin, Malaysia, is a tropical catchment receiving heavy monsoon rainfall coupled with intense land use/land cover (LULC) changes making the area consistently flood prone thereby deteriorating water balance in the area. The most recent is that of December 2014 flood which lead to catastrophic loss of huge amount of properties worth millions of Malaysian ringgit. In view of this, the current research was conducted with the aim of developing (1) tL and Tc based on 1984, 2002, and 2013 LULC conditions; (2) a relationship between tL and tL parameters; and (3) a relationship between tL and Tc among different LULC conditions. Kelantan River basin was first delineated into four major catchments, viz., Galas, Pergau, Lebir, and Nenggiri, due to its large size (approximately 13,100 km2). Soil map and LULC change maps corresponding to 1984, 2002, and 2013 were used for the calculation of CN values while NRCS lag equation was used for the estimation of tL and Tc. The results showed that deforestation for logging activities and agricultural practices were the dominant LULC changes across the watershed. Low values of both tL and Tc were obtained across the catchment which are typical for a tropical monsoon catchment characterized with high runoff and short peak discharge. Results of tL and Tc in this study are not affected by LULC changes in the basin. Slope was observed to be highly correlated with tL. Correlation coefficient was used to determine the relationship between tL and tL factor, and hydraulic length and slope (LS). The results showed high correlation in all the catchments from 1984 to 2013 except for Lebir catchment where a moderate correlation was observed. The findings of this study may be useful to water resource planners in controlling water loss for future planning.


Lag time Time of concentration GIS NRCS-CN method Kelantan River basin Malaysia 

Supplementary material

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  1. Abdulkareem JH, Pradhan B, Sulaiman WNA, Jamil NR (2017) Prediction of spatial soil loss impacted by long-term land-use/land-cover change in a tropical watershed. Geosci Frontiers.
  2. Abdulkareem JH, Pradhan B, Sulaiman WNA, Jamil NR (2018a) Quantification of runoff as influenced by morphometric characteristics in a rural complex catchment. Earth Systems Environ 2:145–162CrossRefGoogle Scholar
  3. Abdulkareem JH, Sulaiman WNA, Pradhan B, Jamil NR (2018b) Relationship between design floods and land use land cover (LULC) changes in a tropical complex catchment. Arab J Geosci 11:376CrossRefGoogle Scholar
  4. Abdulkareem JH, Pradhan B, Sulaiman WNA, Jamil NR (2018c) Long-term runoff dynamics assessment measured through land use/cover (LULC) changes in a tropical complex catchment. Environ Syst.
  5. Adnan NA, Atkinson PM (2011) Exploring the impact of climate and land use changes on streamflow trends in a monsoon catchment. Int J Climatol 31:815–831. CrossRefGoogle Scholar
  6. Amorocho J, Brandstetter A (1971) Determination of nonlinear functional response functions in rainfall-runoff processes. Water Resour Res 7:1087–1101CrossRefGoogle Scholar
  7. Arnold JG, Neitsch SL, Kiniry JR, Williams JR, King KW (2005) Soil and Water Assessment Tool (SWAT) Theoretical Documentation version 2005. Agriculture Research Service US. Texas. [terhubung Berkala]. Http://www. Http. Brc. Tamus. Edu/swat/document. Html [31 Oktober 2008]Google Scholar
  8. Aronica GT, Candela A (2007) Derivation of flood frequency curves in poorly gauged Mediterranean catchments using a simple stochastic hydrological rainfall–runoff model. J Hydrol 347:132–142CrossRefGoogle Scholar
  9. Bonnet MP, Barroux G, Martinez JM, Seyler F, Moreira-Turcq P, Cochonneau G, Melack JM, Boaventura G, Maurice-Bourgoin L, León JG, Roux E, Calmant S, Kosuth P, Guyot JL, Seyler P (2008) Floodplain hydrology in an Amazon floodplain lake (Lago Grande de Curuaí). Hydrol 349(1–2):18–30. CrossRefGoogle Scholar
  10. Bree T (1978) The stability of parameter estimation in the general linear model. J Hydrol 37:47-66. CrossRefGoogle Scholar
  11. Cetin M (2015) Using GIS analysis to assess urban green space in terms of accessibility: case study in Kutahya. Int J Sust Dev World 22:420-424CrossRefGoogle Scholar
  12. Chen Y, Xu Y, Yin Y (2009) Impact of land use change scenarios on storm-runoff generation in Xitiaoxi basin, China. Quatern Int 208(1-8):121-128CrossRefGoogle Scholar
  13. DID (Drainage and Irrigation Department) (2000) Annual flood report of DID for Peninsular Malaysia. Kuala LampurGoogle Scholar
  14. Du S, Shi P, Van Rompaey A, Wen J (2015) Quantifying the impact of impervious surface location on flood peak discharge in urban areas. Nat Haz 76(3):1457-1471CrossRefGoogle Scholar
  15. Eldho TI, Jha A, Singh AK (2006) Integrated watershed modeling using a finite element method and GIS approach. Int J River Basin Manag 4(1):17-25CrossRefGoogle Scholar
  16. Grimaldi S, Petroselli A, Tauro F, Porfiri M (2012) Time of concentration: a paradox in modern hydrology. Hydrol Sci J 57:217-228. CrossRefGoogle Scholar
  17. Jang TI, Kim HK, Im SJ, Park SW (2010) Simulations of storm hydrographs in a mixed-landuse watershed using a modified TR-20 model. Agric Water Manag 97(2):201-207. CrossRefGoogle Scholar
  18. Jun CL, Mohamed ZS, Peik AL, Razali SF, Sharil S (2016) Flood forecasting model using empirical method for a small catchment area. J Eng Sci Technol 11(5):666-672Google Scholar
  19. Koutroulis AG, Tsanis IK (2010) A method for estimating flash flood peak discharge in a poorly gauged basin: case study for the 13-14 January 1994 flood, Giofiros basin, Crete, Greece. J Hydrol 385:150-164. CrossRefGoogle Scholar
  20. Liu YB, Gebremeskel S, De Smedt F, Hoffmann L, Pfister L (2003) A diffusive transport approach for flow routing in GIS-based flood modelling. J Hydrol 283(1-4):91-106. CrossRefGoogle Scholar
  21. Loukas A, Quick MC (1996) Physically-based estimation of lag time for forested mountaneous watersheds. Hydrol Sci J 41:1-19. CrossRefGoogle Scholar
  22. McCuen RH (2009) Uncertainty analyses of watershed time parameters. J Hydraul Eng 14:490-498. CrossRefGoogle Scholar
  23. Noori N, Kalin L, Sen S, Srivastava P, Lebleu C, Hec-hms FÁ (2016) Identifying areas sensitive to land use/land cover change for downstream flooding in a coastal Alabama watershed. Reg Environ Chang 16:1833-1845. CrossRefGoogle Scholar
  24. NRCS (Natural Resource Conservation Service) (1986) Urban hydrology for small watersheds TR-55. USDA Natural Resource Conservation Service, Conservation Engeneering Division Technical Release 55, 164. Technical Release 55Google Scholar
  25. NRCS (Natural Resource Conservation Service) (1997) Time of concentration. National Engineering Handbook, 29Google Scholar
  26. Ozdemir H, Elbaşı E (2015) Benchmarking land use change impacts on direct runoff in ungauged urban watersheds. Phys Chem Earth A/B/C 79:100-107. CrossRefGoogle Scholar
  27. Pradhan B, Youssef AM (2011) A 100-year maximum flood susceptibility mapping using integrated hydrological and hydrodynamic models: Kelantan River Corridor, Malaysia. J Flood Risk Manag 4:189-202. CrossRefGoogle Scholar
  28. Rodríguez-Iturbe I, Valdes JB (1979) The geomorpholic structure of hydrologic response. Water Resour Res 15:1409-1420. CrossRefGoogle Scholar
  29. Rogers WF (1980) A practical model for linear and nonlinear runoff. J Hydrol 46:51-78CrossRefGoogle Scholar
  30. Romshoo SA, Bhat SA, Rashid I (2012) Geoinformatics for assessing the morphometric control on hydrological response at watershed scale in the upper Indus Basin. J Earth Syst Sci 121:659-686. CrossRefGoogle Scholar
  31. SCS (Soil Conservation Service) (1972) National engineering handbook section 4, HydrologyGoogle Scholar
  32. Shrestha S, Khatiwada M, Babel MS, Parajuli K (2014) Impact of climate change on river flow and hydropower production in Kulekhani hydropower project of Nepal. Environ Process 1(3):231-250CrossRefGoogle Scholar
  33. Singh VP, Singh VP (1992) Elementary hydrology. Prentice Hall, Englewood CliffsGoogle Scholar
  34. Soulis KX, Valiantzas JD (2012) SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds—the two-CN system approach. Hydrol Earth Syst Sci 16:1001-1015. CrossRefGoogle Scholar
  35. Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J Hydrol 504:69-79. CrossRefGoogle Scholar
  36. USACE-HEC (United States Army Corp of Engineers-Hydrologic Engineering Center) (2010) Hydrologic modeling system, HEC-HMS user’s manual. United States Army Corp of Engineers, Hydrologic Engineering CentreGoogle Scholar
  37. Wan I (1996) Urban growth determinants for the state of Kelantan for the state’s policy makers. Accessed 4 Apr 2017
  38. Watt EW, Chow AKC (1985) A general expression for basin lag time. Canadian J Civil Eng 12:294-300CrossRefGoogle Scholar
  39. Xia J (2002) Theory and method on nonlinear hydrology system. Wuhan University Press, WuhanGoogle Scholar
  40. Xiao B, Wang QH, Fan J, Han FP, Dai QH (2011) Application of the SCS-CN model to runoff estimation in a small watershed with high spatial heterogeneity. Pedosphere 21:738-749. CrossRefGoogle Scholar
  41. Zhan X, Huang ML (2004) ArcCN-Runoff: an ArcGIS tool for generating curve number and runoff maps. Environ Model Softw 19:875-879. CrossRefGoogle Scholar
  42. Zhang S, Liu C, Yao Z, Guo L (2007) Experimental study on lag time for a small watershed. Hydrol Process 21:1045-1054CrossRefGoogle Scholar
  43. Zope PE, Eldho TI, Jothiprakash V (2015) Impacts of urbanization on flooding of a coastal urban catchment: a case study of Mumbai City, India. Na Haz 75(1):887-908CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Department of Environmental Science, Faculty of Environmental StudiesUniversiti Putra Malaysia (UPM)SerdangMalaysia
  2. 2.Department of Soil Science, Institute for Agricultural Research/Faculty of AgricultureAhmadu Bello UniversityZariaNigeria
  3. 3.The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoAustralia

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