Watershed is the basic unit for studying different hydrologic processes. Flow forecasting in a watershed is dependent upon the rainfall. The effect of erroneous rainfall prediction is a source of uncertainty in flow forecasting. In this study, a model is proposed to improve the flow forecasting on real-time basis. The proposed model has three components (1) Adaptive Grey Rainfall Forecasting Model, (2) Rainfall-Runoff Model and (3) Fuzzy Updating Model. The proposed forecasting model is tested for lead periods of 1 to 3 h with hourly rainfall and discharge data. In this study, four different cases using combination of three models are discussed and the results are compared. The study has been carried out on three Indian watersheds namely Banha, Harsul and Khadakohol. The performance of the model is measured using Nash Sutcliffe Efficiency (E), Correlation Coefficient (r), Error of Peak Discharge (EQpeak) and Error of Time to Peak (ETpeak). It is observed that the case with integration of all three models performed good with a forecasting efficiency of E = 0.950, 0.861, 0.564; and r = 0.991, 0.972, 0.897 for lead-1, 2, 3 respectively for Banha watershed. For Harsul watershed, E = 0.898, 0.704, 0.367; and r = 0.985, 0.949, 0.834 for lead-1, 2, 3 respectively. For Khadakohol watershed, E = 0.968, 0.932, 0.787; and r = 0.994, 0.987, 0.951 for lead-1, 2, 3 respectively. EQpeak is less than 10% for lead-1 for most of the events and increased slightly for lead-2 and lead-3. ETpeak is 0 h for all lead periods of the three watersheds. The proposed model is useful for farmers in planning and monitoring of water resources for crop management and helps in taking necessary actions during heavy rains and floods.
Grey model Real-time forecasting Runoff model Updating model Watershed
This is a preview of subscription content, log in to check access.
Authors are thankful to the DST-WTI, India for the financial assistance to carry out this work through project no. DST/TM/WTI/2 K12/47(G) and Mr. Guy Honore, Project coordinator, Indo German Bilateral Project, for providing data through project no. 03IS007.
The research described in this paper was funded by DST-WTI, India through project no. DST/TM/WTI/2 K12/47(G).
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
Athanasios L, Lampros V (2014) Streamflow simulation methods for ungauged and poorly gauged watershed. Nat Hazards Earth Syst Sci 14:1641–1661CrossRefGoogle Scholar
Burlando P, Rosso R, Cadavid LG, Salas JD (1993) Forecasting of short-term rainfall using ARMA models. J Hydrol 144:193–211CrossRefGoogle Scholar
Chang C-J, Li D-C, Chen C-C, Dai W-L (2013a) A grey-based rolling procedure for short-term forecasting using limited time series data. Economic Computation & Economic Cybernetics studies & Research 47(3)Google Scholar
Hui S, Yang F, Li Z, Liu Q, Dong J (2009) Application of Grey system theory to forecast the growth of larch. International Journal of Information and Systems Sciences 5(3–4):522–527Google Scholar
Jong PK, Won K, Il-Wong J, Gwangseob K (2015) Runoff prediction in Ungauged watersheds using remote sensor datasets. Journal of Water Resource and Hydraulic Engineering 4(3):257–264CrossRefGoogle Scholar
Kang MG, Park SW, Cai X (2009) Integration of hydrologic gray model with global search method for real-time flood forecasting. J Hydrol Eng 14(10):1136–1145CrossRefGoogle Scholar
Kar AK, Winn LL, Lohani AK, Goel NK (2012) Soft Computing-Based Workable Flood Forecasting Model for Ayeyarwady River Basin of Myanmar. Journal of Hydraulic engineering 17(7):807-822CrossRefGoogle Scholar
Kishor C, Balram P, Jagadish CP (2014) Simulation of rainfall-runoff process using HEC-HMS model for Balijore Nala Watershed, Odisha, India. International Journal of Geomatics and Geosciences 5(2)Google Scholar
Lardet P, Obled C (1994) Real time flood forecasting using a stochastic rainfall generator. J Hydrol 162:391–408CrossRefGoogle Scholar
Li DC, Yeh CW (2008) A non-parametric learning algorithm for small manufacturing data sets. Expert Syst Appl 34:391–398CrossRefGoogle Scholar
Li DC, Yeh CW, Chang CJ (2009) An improved Grey-based approach for early manufacturing data forecasting. Comput Ind Eng 57:1161–1167CrossRefGoogle Scholar
Lin GF, Wu MC (2009) A hybrid neural network for typhoon-rainfall forecasting. J Hydrol 375:450–458CrossRefGoogle Scholar
Lin G-F, Jhong B-C (2015) A real-time forecasting model for the spatial distribution of typhoon rainfall. J Hydrol 521:302–313CrossRefGoogle Scholar
Liu J, Wang J, Pan S, Tang K, Li C, Han D (2015) A Real-time flood forecasting system with dual updating of the NWP rainfall and the river flow. Natural Hazards 77(2):1161-1182CrossRefGoogle Scholar
Lohani AK, Goel NK, Bhatia KKS (2005) Development of fuzzy logic based real time flood forecasting system for river Narmada in Central India. International Conference on Innovation Advances and Implementation of Flood Forecasting Technology, TromsoGoogle Scholar
Lohani AK, Goel NK, Bhatia KKS (2014) Improving real time flood forecasting using fuzzy inference system. J Hydrol 509:25–41CrossRefGoogle Scholar
Nayak PC, Sudheer KP, Ramasastri KS (2005) Fuzzy computing based rainfall-runoff model for real time flood forecasting. Hydrol Process 19:955–968CrossRefGoogle Scholar
Nhita F, Adiwijaya (2013) A rainfall forecasting using fuzzy system based on genetic algorithm. International Conference of Information and Communication TechnologyGoogle Scholar
Panwar H, Reddy KV (2015) Optimisation of runoff parameters of a watershed by genetic algorithm and Visualising the runoff in 4D GIS environment. International Journal of Research in Engineering and Technology 4(11):67–71Google Scholar
Reddy KV (2007) Distributed Rainfall-Runoff Modeling of Watershed Using Finite Element Method, Remote Sensing and Geographical Information Systems. PhD Thesis, IIT Bombay, IndiaGoogle Scholar
Reddy KV, Eldho TI, Rao EP (2007) Simulation of runoff from watershed using finite element based kinematic wave model. ISH Journal of Hydraulic Engineering 13(2):15–30CrossRefGoogle Scholar
Reddy KV, Eldho TI, Rao EP, Kulkarni AT (2011) FEM-GIS based channel network model for runoff simulation in agricultural watersheds using remotely sensed data. International Journal of River Basin Management 9:17–30CrossRefGoogle Scholar
Refsgaard JC (1997) Validation and intercomparison of different updating procedures for real-time forecasting. Nord Hydrol 28(2):65–84CrossRefGoogle Scholar
Serban P, Askew AJ (1991) Hydrological forecasting and updating procedures. IAHS Publication No 201:357–369Google Scholar
Shirisha P, Reddy KV, Pratap D (2018) Flow forecasting in a watershed using autoregressive updating model. Water Resour Manag 32(8):2701–2716CrossRefGoogle Scholar
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modelling and control. IEEE Transactions Systems, Man and Cybernetics 15(1):116–132CrossRefGoogle Scholar
Vieux BE, Ester C, Dempsey C, Vieux JE (2002) Prospects for a real-time flood warning system in Arizona. In: Proceedings of the 26th Annual Conference, Breaking the Cycle of Repetitive Flood Loss, Association of State Floodplain Managers, PhoenixGoogle Scholar
Wang Y-F (2002) Predicting stock price using fuzzy grey prediction system. Expert Syst Appl 22:33–39CrossRefGoogle Scholar