Abstract
Hydrological models are vital component and essential tools for water resources and environmental planning and management. In recent times, several studies have been conducted with a view of examining the compatibility of model results with streamflow measurements. Some modelers are of the view that even the use of complex modeling techniques does not give better assessment due to soil heterogeneity and climatic changes that plays vital roles in the behavior of streamflow. In Malaysia, several public domain hydrologic models that range from physically-based models, empirical models and conceptual models are in use. These include hydrologic modeling system (HEC-HMS), soil water assessment tool (SWAT), MIKE-SHE, artificial neural network (ANN). In view of this, a study was conducted to evaluate the hydrological models used in Malaysia, determine the coverage of the hydrological models in major river basins and to identify the methodologies used (specifically model performance and evaluation). The results of the review showed that 65% of the studies conducted used physical-based models, 37% used empirical models while 6% used conceptual models. Of the 65% of physical-based modelling studies, 60% utilized HEC-HMS an open source models, 20% used SWAT (public domain model), 9% used MIKE-SHE, MIKE 11 and MIKE 22, Infoworks RS occupied 7% while TREX and IFAS occupy 2% each. Thus, indicating preference for open access models in Malaysia. In the case of empirical models, 46% from the total of empirical researches in Malaysia used ANN, 13% used Logistic Regression (LR), while Fuzzy logic, Unit Hydrograph, Auto-regressive integrated moving average (ARIMA) model and support vector machine (SVM) contributed 8% each. Whereas the remaining proportion is occupied by Numerical weather prediction (NWP), land surface model (LSM), frequency ratio (FR), decision tree (DT) and weight of evidence (WoE). Majority of the hydrological modelling studies utilized one or more statistical measure of evaluating hydrological model performance (R, R2, NSE, RMSE, MAE, etc.) except in some few cases where no specific method was stated. Of the 70 papers reviewed in this study, 16 did not specify the type of model evaluation criteria they used in evaluating their studies, 17 utilized only one method while 37 used two or more methods. NSE with 27% was found to be the most widely used method of evaluating model performance; R and RMSE came second with a percentage use 24% each. R2 (20%) was recorded as the third most widely used model evaluation criteria in Malaysia, MAE came fourth with 16% while PBIAS is the least with 11%.The findings of this work will serve as a guide to modelers in identifying the type of hydrological model they need to apply to a particular catchment for a particular problem. It will equally help water resources managers and policy makers in providing them with executive summary of hydrological studies and where more input is needed to achieve sustainable development.
Similar content being viewed by others
References
Ab Razak NH, Aris AZ, Ramli MF, Looi LJ, Juahir H (2016) Temporal flood incidence forecasting for Segamat River (Malaysia) using autoregressive integrated moving average modelling. J Flood Risk Manag. https://doi.org/10.1111/jfr3.12258
Abbott MB, Bathurst JC, Cunge JA, O’Connell PE, Rasmussen J (1986a) An introduction to the European Hydrological System—Systeme Hydrologique Europeen, “SHE”, 1: history and philosophy of a physically-based, distributed modelling system. J Hydrol 87:45–59
Abbott MB, Bathurst JC, Cunge JA, O’connell PE, Rasmussen J (1986b) An introduction to the European Hydrological System—Systeme Hydrologique Europeen,“SHE”, 2: Structure of a physically-based, distributed modelling system. J Hydrol 87:61–77
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 Front. https://doi.org/10.1016/j.gsf.2017.10.010
Abdulkareem JH, Sulaiman WNA, Pradhan B, NR Jamil (2018a) Relationship between design floods and land use land cover (LULC) changes in a tropical complex catchment. Arab J Geosci 11:376. https://doi.org/10.1007/s12517-018-3702-4
Abdulkareem JH, Pradhan B, Sulaiman WNA, Jamil NR (2018b) Quantification of runoff as influenced by morphometric characteristics in a rural complex catchment. Earth Syst Environ 2:145–162. https://doi.org/10.1007/s41748-018-0043-0
Abdulkareem JH, Sulaiman WNA, Pradhan B, Jamil NR (2018c) Long-term hydrologic impact assessment of non-point source pollution measured through land use/land cover (LULC) changes in a tropical complex catchment. Earth Syst Environ. https://doi.org/10.1007/s41748-018-0042-1
Abdulkareem JH, Pradhan B, Sulaiman WNA, Jamil NR (2018d) Long-term runoff dynamics assessment measured through land use/cover (LULC) changes in a tropical complex catchment. Environ Syst. https://doi.org/10.1007/s10669-018-9696-3
Abdullah J (2013) Distributed Runoff Simulation of Extreme Monsoon Rainstorms in Malaysia Using TREX. Dissertation Colorado State University Fort Collins, Colorado
Abood MM, Mohammed TA, Ghazali AH, Mahmud AR, Sidek LM (2012) Impact of infiltration methods on the accuracy of rainfall-runoff simulation. Appl Sci Eng Tech 4:1708–1713. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862063367&partnerID=40&md5=a9fb9185b81a9fe7328e7b44b5afea25. Accessed 12 Mar 2017
Addiscott TM, Whitmore AP (1987) Computer simulation of changes in soil mineral nitrogen and crop nitrogen during autumn, winter and spring. J Agric Sci 109:141–157
Adenan NH, Noorani MSM (2016) Multiple time-scales nonlinear prediction of river flow using chaos approach. J Technol 78:1–7
Adnan NA, Atkinson PM (2018) Disentangling the effects of long-term changes in precipitation and land use on hydrological response in a monsoonal catchment. J Flood Risk Manag 11:1063–1077
Adnan NA, Basarudin Z, Che Omar N (2014a) Variation in hydrological responses estimation simulations due to land use changes. In: International conference on civil, biological and environmental engineering (CBEE), Istanbul, Turkey, pp. 27–28
Adnan R, Samad AM, Zain ZM, Ruslan FA (2014b) 5 hours flood prediction modeling using improved NNARX structure: case study Kuala Lumpur. In: Proceedings of the 2014 IEEE 4th international conference on system engineering and technology, ICSET 2014, pp 5–9. https://doi.org/10.1109/ICSEngT.2014.7111799
Agresti A (2002) Categorical data analysis (vol 2). Wiley, New York
Alaghmand S, Abdullah R, Abustan I, Said MA, Vosoogh B (2012) GIS-based river basin flood modelling using HEC-HMS and MIKE 11—Kayu Ara River basin, Malaysia. J Environ Hydrol 20:1–16
Alam MJ, Meah MA, Noor MS (2011) Numerical modeling of ground water flow and the effect of boundary conditions for the hsieh aquifer. Asian J Math Stat 4:33–44
Alansi AW, Amin MSM, Abdul Halim G, Shafri HZM, Aimrun W (2009) Validation of SWAT model for stream flow simulation and forecasting in Upper Bernam humid tropical river basin, Malaysia. Hydrol Earth Syst Sci 6:7581–7609. https://doi.org/10.5194/hessd-6-7581-2009
Ali MF, Rahman NFA, Khalid K (2014) Discharge assessment by using integrated hydrologic model for environmental technology development. Adv Mat Res 911:378–382. http://doi.org/10.4028/www.scientific.net/AMR.911.378
Amini A, Ali TM, Ghazali AHB, Aziz AA, Akib SM (2011) Impacts of land-use change on streamflows in the Damansara watershed, Malaysia. Arab J Sci Eng 36:713–720. https://doi.org/10.1007/s13369-011-0075-3
Andersen J, Refsgaard JC, Jensen KH (2001) Distributed hydrological modelling of the Senegal River basin—Model construction and validation. J Hydrol 247:200–214. https://doi.org/10.1016/S0022-1694(01)00384-5
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.Brc.Tamus.Edu/swat/document.html. Accessed 31 Oct 2008
Asmat A, Mansor S, Saadatkhah N, Adnan NA, Khuzaimah Z (2016) Land use change effects on extreme flood in the Kelantan basin using hydrological model. In: Tahir W, Abu Bakar PA, Wahid M, Mohd Nasir SM, Lee W (eds) ISFRAM 2015. Springer, Singapore, pp 221–236
Ayub KR, Hin LS, Aziz HA, Ampangan S, Tebal N, Perai S et al (2009) SWAT application for hydrologic and water quality modeling for suspended sediments: a case study of Sungai Langat catchment in Selangor. In: International conference on water resources (ICWR 2009), pp 26–27
Bárdossy A (2006) Calibration of hydrological model parameters for ungauged catchments. Hydrol Earth Syst Sci Dis 3:1105–1124. https://doi.org/10.5194/hessd-3-1105-2006
Basarudin Z, Adnan NA, Latif ARA, Tahir W, Syafiqah N (2014) Event-based rainfall-runoff modelling of the Kelantan River basin. IOP Conf Ser Earth Environ Sci 18:12084. https://doi.org/10.1088/1755-1315/18/1/012084
Bathurst JC, Ewen J, Parkin G, O’Connell PE, Cooper JD (2004) Validation of catchment models for predicting land-use and climate change impacts. 3. Blind validation for internal and outlet responses. J Hydrol 287:74–94. https://doi.org/10.1016/j.jhydrol.2003.09.021
Beheshti Z, Firouzi M, Shamsuddin SM, Zibarzani M, Yusop Z (2016) A new rainfall forecasting model using the CAPSO algorithm and an artificial neural network. Neural Comput Appl 27:2551–2565. https://doi.org/10.1007/s00521-015-2024-7
Bellocchi G, Rivington M, Donatelli M, Matthews K, Bellocchi G, Rivington M et al (2010) Validation of biophysical models: issues and methodologies. Sustain Agric 2:577–603. https://doi.org/10.1051/agro/2009001
Boughton W (2006) Calibrations of a daily rainfall-runoff model with poor quality data. Environ Modell Softw 21:1114–1128. https://doi.org/10.1016/j.envsoft.2005.05.011
Box GEP, Jenkins GM (1970) Time series analysis: forecasting and control, 1976. ISBN: 0-8162-1104-3
Chang TK, Talei A, Alaghmand S, Ooi MPL (2017) Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques. J Hydrol 545:100–108. https://doi.org/10.1016/j.jhydrol.2016.12.024
Chong FS, Tan DNK (1986) Hydrogeological activities in Peninsular Malaysia and Sarawak. In: Geosea V proceedings, vol II, Geological Society Malaysia. Bulletin vol 20, pp 827–842
Chow VT, Maidment DR, Mays LW (1988) Applied hydrology, pp 12–34
Devi GK, Ganasri BP, Dwarakish GS (2015) A Review on hydrological models. In: International conference on water resources, coastal and ocean engineering (ICWRCOE), pp 1001–1007. https://doi.org/10.1016/j.aqpro.2015.02.126
Dhanoa MS, Lister SJ, France J, Barnes RJ (1999) Use of mean square prediction error analysis and reproducibility measures to study near infrared calibration equation performance. J Near Infrared Spec 7:133–144
DID (Drainage and Irrigation Department) (1974) Hydrological regions of peninsular Malaysia
Dlamini NS, Rowshon MK, Fikhri A, Lai SH, Mohd MSF (2017) Modelling the streamflow of a river basin using enhanced hydro-meteorological data in Malaysia. Acta Hort 1152:291–298. https://doi.org/10.17660/ActaHortic.2017.1152.39
Dooge J (1959) A general theory of the unit hydrograph. J Geoph Res 64:241–256
Engel BA, Flanagan DC (2006) Modeling and risk analysis of nonpoint-source pollution caused by atrazine using SWAT. Trans ASABE 49:667–678
Fleming G (1972) Computer simulation techniques in hydrology. In: George F (ed) Computer simulation techniques in hydrology. Elsevier, Netherlands, pp 48–87
Fox DG (1981) Judging air quality model performance. Bull Am Meteorol Soc 62:599–609
Ghorbani K, Wayayok A, Abdullah AF (2016) Simulation of flood risk area in Kelantan watershed, Malaysia using numerical model. Jurnal Teknol 78:51–57
Goh YC, Zainol Z, Mat Amin MZ (2016) Assessment of future water availability under the changing climate: case study of Klang River Basin, Malaysia. Intern J River Basin Manag 5124:1–9. https://doi.org/10.1080/15715124.2015.1068178
Green IRA, Stephenson D (1986) Criteria for comparison of single event models. Hydrol Sci J 31:395–411
Gupta HV, Sorooshian S, Yapo PO (1999) Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J Hydrol Eng 4:135–143
Hafiz I, Sidek LM, Basri H, Fukami K, Hanapi MN, Livia L, Jaafar AS (2014) Integrated flood analysis system (IFAS) for Kelantan River basin. In: IEEE 2nd international symposium on telecommunication technologies (ISTT), pp 159–162
Halwatura D, Najim MMM (2013) Environmental modelling & software application of the HEC-HMS model for runoff simulation in a tropical catchment. Environ Modell Softw 46:155–162. https://doi.org/10.1016/j.envsoft.2013.03.006
Hasan ZA, Hamidon N, Suffian M (2009) Integrated river basin management (IRBM): hydrologic modelling using HEC-HMS for Sungai Kurau basin, Perak. In: International conference on water resources (ICWR 2009), pp 1147–1152. https://doi.org/10.1128/JVI.03540-12
Hassan Z, Shamsudin S, Harun S, Malek MA, Hamidon N (2015) Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: a case study in the northern area of Peninsular Malaysia. Environ Earth Sci 74:463–477. https://doi.org/10.1007/s12665-015-4054-y
Haykin S (1999) Neural networks. Prentice Hall, New Jersey
Heng CL (2004) Groundwater utilisation and management in Malaysia. 41 St CCOP Annual Session 15–18 November 2004 Tsukuba, Japan, 83
Jaafar K, Ismail N, Tajjudin M, Adnan R, Hezri M, Rahiman F (2016) Hidden neuron variation in multi-layer perceptron for flood water level prediction at Kusial station faculty of electrical engineering. In: 2016 IEEE 12th international colloquium on signal processing & its applications (CSPA2016), 4–6 March 2016, Melaka, Malaysia, pp 4–6. https://doi.org/10.1109/CSPA.2016.7515858
Jajarmizadeh M, Harun S, Salarpour M (2012) A Review on theoretical consideration and types of models in hydrology. J Eng Sci Tech 5:249–261
Jakeman AJ, Letcher RA, Norton JP (2006) Ten iterative steps in development and evaluation of environmental models. Environ Modell Softw 21:602–614. https://doi.org/10.1016/j.envsoft.2006.01.004
Johansson EM, Dowla FU, Goodman DM (1991) Backpropagation learning for multilayer feed-forward neural networks using the conjugate gradient method. Int J Neural Syst 2:291–301
Juahir H, Sultan U, Abidin Z, Azid A, Sultan U, Abidin Z et al (2015) Flood risk pattern recognition by using environmetric technique: a case study in Langat River basin. Jurnal Teknol. https://doi.org/10.11113/jt.v77.4142
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 11:666–672
Kabiri R, Chan A, Bai R (2013) Comparison of SCS and Green-Ampt methods in surface runoff-flooding simulation for Klang watershed in Malaysia. Open J Modern Hydrol 3:102–114. https://doi.org/10.4236/ojmh.2013.33014
Khalid K, Ali MF, Rahman NFA (2015) The development and application of Malaysian soil taxonomy in SWAT watershed model. In: Abu Bakar S, Tahir W, Wahid M, Mohd Nasir S, Hassan R (eds) ISFRAM 2014. Springer, Singapore, pp 77–88. https://doi.org/10.1007/978-981-287-365-1
Khalid K, Ali MF, Faiza N, Rahman A, Mispan MR, Haron SH et al (2016a) Application of the SWAT hydrologic model in Malaysia: recent research. In: The challenges of agro-environmental research in Monsoon Asia, pp 237–246
Khalid K, Ali MF, Rahman NFA, Othman Z, Bachok MF (2016b) Calibration assessment of the distributed hydrologic model using SWAT-CUP. In: Regional conference on science, technology and social sciences (RCSTSS 2016). Springer, Singapore, pp. 241–250
Khalid K, Ali MF, Rahman NFA, Mispan MR (2016c) Application on one-at-a-time sensitivity analysis of semi-distributed hydrological model in tropical watershed. IACSIT Int J Eng Technol 8:132–136. https://doi.org/10.7763/IJET.2016.V8.872
Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67:251–264. https://doi.org/10.1007/s12665-011-1504-z
Krause P, Boyle DP, Bäse F (2005) Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97
Kuok KK, Chiu PC, Chung CM, Chuang TH (2010) Relationship between storage coefficient and catchment area using HEC-HMS for Southern Region of Sarawak. IUP J Soil Water Sci 3:20–33
Kura NU, Ramli MF, Sulaiman WNA, Ibrahim S, Aris AZ (2015) An overview of groundwater chemistry studies in Malaysia. Adv Environ Chem Pollut. https://doi.org/10.1007/s11356-015-5957-6
Kwin CT, Talei A, Alaghmand S, Chua LHC (2016) Rainfall-runoff modeling using dynamic evolving neural fuzzy inference system with online learning. Proc Eng 154:1103–1109. https://doi.org/10.1016/j.proeng.2016.07.518
Legates DR, McCabe GJ (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241
Lippmann R (1987) An introduction to computing with neural nets. IEEE Assp Mag 4:4–22
Lu J, Sun G, Amatya DM, Harder SV, McNulty SG (2006) Understanding the hydrologic response of a coastal plain watershed to forest management and climate change in South Carolina, USA. In: Hydrology and management of forested wetlands, proceedings of the international conference, April 8–12, 2006, New Bern, North Carolina (pp 28). American Society of Agricultural and Biological Engineers
Mah DYS, Putuhena FJ, Said S (2007) Use of infoworks river simulation (RS) In Sungai Sarawak Kanan modeling. J Inst Eng Malays 68:1–9
Mah DYS, Putuhena FJ, Lai SH (2011) Modelling the flood vulnerability of deltaic Kuching City, Malaysia. Nat Hazards 58:865–875. https://doi.org/10.1007/s11069-011-9731-x
Mah DYS, Hii CP, Kho A (2017) Modelling approaches for minimally gauged Sg Similajau, Bintulu. J App Sci Proc Eng 4:2 e-ISSN: 2289–7771
Malek MA, Heyrani M, Juneng L (2015) Stream flow projection for Muar river in Malaysia using precis-HEC-HMS model. ASM Sci J 1:8–19
Manaf LA, Samah MAA, Zukki NIM (2009) Municipal solid waste management in Malaysia: Practices and challenges. Waste Manag 29:2902–2906
McCuen RH (1998) Hydrologic analysis and design. Prentice Hall, New Jersey
McCuen RH, Knight Z, Cutter AG (2006) Evaluation of the Nash–Sutcliffe efficiency Index. J Hydrol Eng 11:597–602. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:6(597)
Mohd MSF, Mispan MR, Juneng L, Tangang FT, Rahman NFA, Khalid K, Rashid MZA, Haron SH (2015) Assessment of impacts of climate change on road transport. ARPN J Eng Appl Sci 10:6634–6642
Mohtar ISA, Tahir W, Bakar SHA, Zuhari AZM (2015) Use of numerical weather prediction model and visible weather satellite images for flood forecasting at Kelantan river basin. In: Bakar S, Tahir W, Wahid M, Nasir S, Hassan R (eds) ISFRAM 2014. Springer, Singapore, pp 283–294. https://doi.org/10.1007/978-981-287-365-1
Moriasi D, Wilson B (2012) Hydrologic and water quality models: use, calibration, and validation. Trans ASABE 55:1241–1247. https://doi.org/10.13031/2013.42265
Moriasi DN, Arnold JG, Liew MW et al (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900
Mustafa YM, Amin MSM, Lee TS, Shariff ARM (2012) Evaluation of land development impact on a tropical watershed hydrology using remote sensing and GIS. J Spat Hydrol 5:16–30
Mustafa Z, Awang A, Abdullah A (2018) Effects of tides on the hydrology and geometry of a freshwater channel J Fundamental Appl Sci. https://doi.org/10.4314/jfas.v10i1s.56 (ISSN 1112–9867)
Nash JE (1959) Systematic determination of unit hydrograph parameters. J Geoph Res 64:111. https://doi.org/10.1029/JZ064i001p00111
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Nasir KAM, Hashim NB, Kazemi Z, Aslani H (2015) Application of the HEC-HMS model for storm events case study: Pendas River, Malaysia. In: International congress on civil engineering, architecture and urban development
Nejadhashemi AP, Wardynski BJ, Munoz JD (2011) Evaluating the impacts of land use changes on hydrologic responses in the agricultural regions of Michigan and Wisconsin. Hydrol Earth Syst Sci Disc 8:3421–3468. https://doi.org/10.5194/hessd-8-3421-2011
Nor NI, Harun S, Kassim AH (2007) Radial basis function modeling of hourly streamflow hydrograph. J Hydrol Eng 12:113–123. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:1(113)
Norzilah A, Ahmad MF, Jusoh A, Tofany N, Yaacob R, Muslim AM (2016) Hydrodynamics modelling at Setiu wetland, Terengganu. J Environ Sci Tech 9:437–445. https://doi.org/10.3923/jest.2016.437.445
Omang SA, Tahir SH (1994) Cretaceous and Neogene lavas of Sabah origin and tectonic significance Kuala Terengganu, Malaysia. Geol Soc Malay Bull 21–30
Penman HL (1961) Weather, plant and soil factors in hydrology. Weather 16:207–219
Perera EDP, Lahat L (2014) Fuzzy logic based flood forecasting model for the Kelantan River basin, Malaysia. J Hydro-Environ Res. https://doi.org/10.1016/j.jher.2014.12.001
Pradhan B (2009) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9:2
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. https://doi.org/10.1111/j.1753-318X.2011.01103.x
Pradhan B, Shafiee M, Pirasteh S (2009) Maximum flood prone area map RADARSAT images and GIS: Kelantan River basin. Intern J Geomat 5:11–23
Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical recipes in Fortran 77: the art of scientific computing. Cambridge university press, Cambridge
Rabuñal JR, Puertas J, Suárez J, River D (2007) Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks. Hydrol Proc 21:476–485. https://doi.org/10.1002/hyp
Rahim BEE, Yusoff I, Jafri AM, Othman Z, Abdul Ghani A (2012) Application of MIKE SHE modelling system to set up a detailed water balance computation. Water Environ J 26:490–503. https://doi.org/10.1111/j.1747-6593.2012.00309.x
Ramly S, Tahir W (2016) Application of HEC-GeoHMS and HEC-HMS as rainfall–runoff model for flood simulation. ISFRAM 2015. Springer, Singapore, pp 181–192. https://doi.org/10.1007/978-981-10-0500-8_15
Ray K (1975) Hydrology for engineers. McGraw Hill, Kogakusha
Razi MAM, Ariffin J, Tahir W, Arish NAM (2010) Flood estimation studies using hydrologic modeling system (HEC-HMS) for Johor River, Malaysia. J Appl Sci 10:930–939
Refsgaard JC (1996) The role of distributed hydrological modelling in water resources management. In: Abbott MB, Refsgaard JC (eds) Distributed hydrological modelling. Springer, Germany. https://doi.org/10.1007/978-94-009-0257-2
Refsgaard JC, Storm B (1995) MIKE SHE. Comput Models Watershed Hydrol 1:809–846
Romali NS, Yusop Z, Ismail AZ (2018) Hydrological modelling using HEC-HMS for flood risk assessment of Segamat Town, Malaysia. IOP Conf Ser Mater Sci Eng 318(1):012029
Romaly NS, Yusop Z, Ismail AZ (2018) Application of HEC-RAS and Arc GIS for floodplain mapping in Segamat town, Malaysia. Intern J GEOMATE 2186–2990 https://doi.org/10.21660/2018.43.3656
Ros FC, Sidek LM, Ibrahim NNN, Razad AA (2008) Probable maximum flood (PMF) for the Kenyir Catchment, Malaysia. In: International conference on construction and building technology, pp 325–334
Saadatkhah N, Tehrani MH, Mansor S, Khuzaimah Z, Kassim A, Saadatkhah R (2016) Impact assessment of land cover changes on the runoff changes on the extreme flood events in the Kelantan River basin. Arab J Geos 9:687. https://doi.org/10.1007/s12517-016-2716-z
Saghafian B, Khosroshahi M (2005) Unit response approach for priority determination of flood source areas. J Hydrol Eng 10:270–277. https://doi.org/10.1061/(ASCE)1084-0699(2005)10:4(270)
Saghafian B, Farazjoo H, Bozorgy B, Yazdandoost F (2008) Flood intensification due to changes in land use. Water Resour Manag 22:1051–1067. https://doi.org/10.1007/s11269-007-9210-z
Santhi C, Arnold JG, Williams JR, Hauck LM, Dugas WA (2001) Application of a watershed model to evaluate management effects on point and nonpoint source pollution. Trans ASABE 44:1559–1570
Shamsudin S, Dan’azumi S, Ab Rahman A (2011) Uncertainty analysis of HEC-HMS model parameters using Monte Carlo simulation. Intern J Modell Simul 31:279–286
Shaw EM, Beven KJ, Chappell NA, Lamb R (2010) Hydrology in practice. CRC Press, New York
Sherman LK (1932) Streamflow from rainfall by the unit-graph method. Eng News Record 108:501–505
Singh VP (1995) Computer models of watershed hydrology, vol 1130. Water Resources Publications, Highlands Ranch, CO
Singh VP, Woolhiser DA (2002) Mathematical modeling of watershed hydrology. J Hydrol Eng 7:270–292
Sulaiman WNA, Heshmatpoor A, Rosli MH (2010) Identification of flood source areas in Pahang River basin. Penins Malays Environ Asia 3:73–78
Sulaiman M, El-Shafie A, Karim O, Basri H (2011) Improved water level forecasting performance by using optimal steepness coefficients in an artificial neural network. Water Resour Manag 25:2525–2541. https://doi.org/10.1007/s11269-011-9824-z
Sulaiman AHA, Katimon A, Darus IZM, Shahid S (2016) TOPMODEL for streamflow simulation of a tropical catchment using different resolutions of ASTER DEM: optimization through response surface methodology. Water Resour Manag 30:3159–3173. https://doi.org/10.1007/s11269-016-1338-2
Suparta W, Putro WS, Singh MSJ, Asillam MF (2015) The estimation of rainfall and precipitation variation during 2011 convective system using an artificial neural network over Tawau, Sabah. In: International conference on space science and communication, IconSpace, pp 479–484. https://doi.org/10.1109/IconSpace.2015.7283806
Tahir W, Hamid HAC (2013) Flood forecasting using tank model and weather surveillance radar (WSR) input for Sg Gombak. Intern J Civ Environ Eng 13:42–46. http://www.ijens.org/Vol_13_I_02/133202-5757-IJCEE-IJENS.pdf. Accessed 28 Nov 2015
Tan ML, Ibrahim AL, Yusop Z, Duan Z, Ling L (2014) Impacts of land-use and climate variability on hydrological components in the Johor River basin, Malaysia. Hydrol Sci J 60:873–889. https://doi.org/10.1080/02626667.2014.967246 (141217125340005)
Tayebiyan A, Mohammad TA, Ghazali AH, Mashohor S (2016) Artificial neural network for modelling rainfall-runoff. Pertanika J Sci Tech 24:319–330. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974678275&partnerID=40&md5=aa6e6166c170cda304b4434d38cf306c
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. https://doi.org/10.1016/j.jhydrol.2013.09.034
Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008
Tehrany MS, Pradhan B, Jebur MN (2015) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Stoch Environ Res Risk Assess 512:332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008
Todini E, Wallis JR (1977) Using CLS for daily or longer period rainfall-runoff modelling. Math Models Surf Water Hydrol 100:149–168
UNPF (2015) United Nations Population Fund state of the world population 2015: unlasshing the potential of urban growth. New York City, United States
USACE-HEC (2000) Hydrologic modeling system HEC-HMS: technical reference manual. California, USA
USACE-HEC (2010) Hydrologic modeling system, HEC-HMS user’s manual. United States Army Corp of Engineers, Hydrologic Engineering Centre
Verma AK, Jha MK, Mahana RK (2010) Evaluation of HEC-HMS and WEPP for simulating watershed runoff using remote sensing and geographical information system. Paddy Water Environ 8:131–144. https://doi.org/10.1007/s10333-009-0192-8
Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63:1309–1313
Wong CL, Venneker R, Uhlenbrook S (2010) Analysis and modelling of runoff from two distinct river basins in Peninsular Malaysia. In: HydroPredict conference, p 11
Wong CL, Venneker R, Jamil ABM, Uhlenbrook S (2011) Development of a gridded daily hydrometeorological data set for Peninsular Malaysia. Hydrol Proc 25:1009–1020. https://doi.org/10.1002/hyp.7654
Woolhiser DA (1973) Hydrologic and watershed modeling–state of the art. Trans ASABE 16:553–559
Wurbs RA (1998) Dissemination of generalized water resources models in the United States. Water Intern 23:190–198
Yaseen ZM, El-Shafie A, Afan HA, Hameed M, Mohtar WHMW, Hussain A (2016) RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia. Neural Comput App 27:1533–1542. https://doi.org/10.1007/s00521-015-1952-6
Yeganeh N, Sabri S (2014) Flood vulnerability assessment in Iskandar Malaysia using multi-criteria evaluation and fuzzy logic. J App Sci Eng Technol 8:1794–1806
Yusop Z, Chan CH, Katimon A (2007) Runoff characteristics and application of HEC-HMS for modelling stormflow hydrograph in an oil palm catchment. Water Sci Tech 56:41. https://doi.org/10.2166/wst.2007.690
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Abdulkareem, J.H., Pradhan, B., Sulaiman, W.N.A. et al. Review of studies on hydrological modelling in Malaysia. Model. Earth Syst. Environ. 4, 1577–1605 (2018). https://doi.org/10.1007/s40808-018-0509-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40808-018-0509-y