Generation of Synthetic Streamflow of Jakham River, Rajasthan Using Thomas-Fiering Model

Priyanka Sharma; S. R. Bhakar; Shakir Ali; H. K. Jain; P. K. Singh; Mahesh Kothari

doi:10.52151/jae2018554.1668

Authors

Priyanka Sharma Department of Soil and Water Engineering, CTAE, MPUAT, Udaipur Author
S. R. Bhakar Department of Soil and Water Engineering, CTAE, MPUAT Author
Shakir Ali ICAR- IISWC, Dadwara, Kota Author
H. K. Jain Department of Statistics and Computer Applications, RCA, MPUAT, Udaipur Author
P. K. Singh Department of Soil and Water Engineering, CTAE, MPUAT, Udaipurur Author
Mahesh Kothari Department of Soil and Water Engineering, CTAE, MPUAT, Udaipur Author

DOI:

https://doi.org/10.52151/jae2018554.1668

Keywords:

Streamflow, performance evaluation indices, Thomas-Fiering model, Jakham river

Abstract

A study was undertaken to develop and apply Thomas-Fiering (T-F) model for generation of synthetic streamflow of Jakham river, Rajasthan. Streamflow data of 40 years (June,1975 to May, 2015) was used, of which 360 month-data (June,1975 to May, 2005) were used to develop the model and 120 month-data (June, 2005 to May, 2015) were used to predict the streamflow. Performance of the model was evaluated using four statistical criteria, viz. correlation coefficient (R), root mean square error (RMSE), modified NashSutcliffe efficiency (MNSE), and modified index of agreement (MIA).The results showed satisfactory performance of the model in generating synthetic monthly streamflow based on R, RMSE, MNSE and MIA values of 0.73, 28.47×106 m3 , 0.456 and 0.73, respectively. The developed model was used to generate 100-year synthetic streamflows.

Author Biographies

Priyanka Sharma, Department of Soil and Water Engineering, CTAE, MPUAT, Udaipur

Research Scholar
S. R. Bhakar, Department of Soil and Water Engineering, CTAE, MPUAT

Professor
Shakir Ali, ICAR- IISWC, Dadwara, Kota

Principal Scientist,
H. K. Jain, Department of Statistics and Computer Applications, RCA, MPUAT, Udaipur

Professor,
P. K. Singh, Department of Soil and Water Engineering, CTAE, MPUAT, Udaipurur

Professor,
Mahesh Kothari, Department of Soil and Water Engineering, CTAE, MPUAT, Udaipur

Professor

References

Ahmed J A; Sarma A K. 2007. Artificial neural network model for synthetic streamflow generation. Water Resour. Manage.,21(6), 1015-1029.

Alfa M I; Ajibike M A; Adie D B. 2018. Reliability assessment of Thomas Fiering's method of streamflow prediction. Niger. J. Technol., 37(3), 818-823.

Arselan C A. 2012. Stream flow simulation and synthetic flow calculation by modified Thomas Fiering model. Al-Rafadain Eng. J., 20(4), 118-127.

Baum R; Characklis G W; Serre M L. 2018. Effects of geographic diversification on risk pooling to mitigate drought-related financial losses for water utilities. Water Resour. Res., 54, 2561-2579.

Butts M B; Payne J T; Kristensen M; Madsen H. 2004. An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation. J. Hydrol., 298(1-4), 242-266.

Chandniha S K; Meshram S G; Adamowski J F; Meshram C. 2017. Trend analysis of precipitation in Jharkhand State, India. Theor. Appl. Climatol., 130(1- 2), 261-274.

Chen Huey-Long; Rao A R. 2002. Testing hydrologic time series for stationarity. J. Hydrol. Eng., 7(2), 129- 136.

Cui Q; Wang X; Li C; Cai Y; Liang P. 2016. Improved Thomas–Fiering and wavelet neural network models for cumulative errors reduction in reservoir inflow forecast. J. Hydro-environ. Res., 13, 134-143.

Dashora I; Singal S K; Srivastav D K. 2015. Software application for data driven prediction models for intermittent streamflow for Narmada river basin. Int. J. Comput. Appl.,113(10), 9-17.

De Fraiture C; Giordano M; Liao Y. 2008. Biofuels and implications for agricultural water use: blue impacts of green energy. Water Policy, 10, 67-81.

Fernandez G P; Chescheir G M; Skaggs R W; Amatya D M. 2005. Development and testing of watershed-scale models for poorly drained soils. Trans. ASAE, 48(2), 639-652.

Harms A A; Campbell T H. 1967. An extension to the Thomase Fiering model for the sequential generation of streamflow. Water Resour. Res., 3, 653-661.

Jain S K; Agarwal P K; Singh V P. 2007. Hydrology and Water Resources of India. Springer, The Netherlands, 592.

Jawari M. 2016. Trend and homogeneity analysis of precipitation in Iran. Clim., 14(4), 1-23.

Jothiprakash V; Magar R B. 2012. Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data. J. Hydrol., 450, 293-307.

Joshi G S; Gupta K. 2009. A simulation model for the operation of multipurpose multi-reservoir system for river Narmada. India. J. Hydro-environ. Res., 3, 96-108.

Kurunç A; Yürekli K; Çevik O. 2005. Performance of two stochastic approaches for forecasting water quality and streamflow data from Yeşilirmak river, Turkey. Environ. Model Softw., 20, 1195-1200.

Machiwal D; Jha M K. 2003. Optimal land and water resources allocation using stochastic linear programming. In: Singh P Vijay; Yadava Ram Narayan. Water Resources System Operation: Proceedings of the International Conference on Water and Environment (WE-2003), December 15-18, Bhopal, India. Allied Publishers, 5, 264-275.

Machiwal D; Jha M K. 2012. Hydrologic Time Series Analysis: Theory and Practice. Springer, the Netherlands and Capital Publishing Company, New Delhi, India, pp:303.

Machiwal D; Jha M K. 2015. GIS-based water balance modeling for estimating regional specific yield and distributed recharge in data-scarce hard-rock regions. J. Hydro-environ. Res., 9(4), 554-568.

Majid K; Arash M. 2018. Homogeneity analysis of stream flow records in arid and semi-arid regions of north western Iran. J. Arid Land, 10(4), 493-506.

McMahon T A; Miller A J. 1971. Application of the Thomas and Fiering model to skewed hydrologic data. Water Resour. Res. 7, 1338-1340.

Mezaache M; Mansouri R. 2015. Assessment of reservoir behaviour: a case study of the Bouhamdane Dam. Int. J. Water,9(4), 376-394.

Moravej M; Khalili K. 2015. Hydrological time series analysis and modeling using statistical tests and linear time series models (case study: West Azerbaijan province of Iran). Int. J. Hydrol. Sci. Technol., 5(4), 349-371.

Nourani V; Mogaddam A A; Nadiri A O. 2008. An ANN-based model for spatiotemporal groundwater level forecasting. Hydrol. Process, 22 (26), 5054-5066.

Ray M R; Sarma A K. 2011. Importance of input parameter selection for synthetic streamflow generation of different time step using ANN techniques. In: XI International Joint Conference on Computational Intelligence (Neural Computation Theory and Applications), Paris, France, 24-26 October, 211-217.

Ray M R; Sarma A K. 2016. Influence of time discretization and input parameter on the ANN based synthetic streamflow generation. Water Resour. Manage. 30(13), 4695-4711.

Rezaeian-Zadeh M; Tabari H; Abghari H. 2013. Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions. Arab J. Geosci., 6(7), 2529-2537.

Riad S; Mania J; Bouchaou L. 2004. Predicting catchment flow in a semiarid region via an artificial neural network technique. Hydrol. Process., 18(13), 2387-2393.

Sathish S; Babu S K. 2017. Stochastic time series analysis of hydrology data for water resources. In: IOP Conference Series, Mater. Sci. Eng., 263(4),1-6.

Sudheer K P; Nayak P C; Ramasastri K S. 2003. Improving peak flow estimates in artificial neural network river flow models. Hydrol. Process., 17(3), 677-686.

Stedinger J R; Taylor M R. 1982. Synthetic streamflow generation, 1. Model verification and validation. Water Resour. Res., 18(4), 909-918.

Thomas H A; Fiering M B. 1962. Mathematical synthesis of streamflow sequences for the analysis of river basins by simulation. In: Maass A; Marglin S; Fair G (Eds.), Design of Water Resources Systems. Harvard University Press, Massachusetts, 459-493.

Vaghela C R; Vaghela A R. 2014. Synthetic flow generation. Int. J. Eng. Res. Appl., 4(7), 66-71.

Wang W C; Chau K W; Cheng C T; Qiu L. 2009. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J. Hydrol., 374(3-4), 294-306.

Wang Y; Zheng T; Zhao Y; Jiang J; Wang Y; Guo L; Wang P. 2013. Monthly water quality forecasting and uncertainty assessment via bootstrapped wavelet neural networks under missing data for Harbin, China. Environ. Sci. Pollut. Res., 20, 8909-8923.

Wijayaratne L H; Chan P C. 1987. Synthetic Flow Generation with Stochastic Models. In: Singh V P (Eds.), Flood Hydrology, Springer, Dordrecht, 175- 185.