Assessment of Stream Flow of Hidkal Dam Catchment Area in Krishna Basin of India Using SIMHYD Model

I. Bhaskara Rao *

ANGRAU- Reginal Agricultural Research Station, Nandyal, Andhra Pradesh, India.

M. Nemichandrappa

Department of Agricultural Engineering, College of Agriculture Engineering, UAS, Raichur, Karnataka, India.

K. V. Rao

ICAR-CRIDA, Hyderabad, Telangana, India.

B. S. Polisgowdar

Department of Irrigation and Drainage, College of Agril. Engg., UAS, Raichur, Karnataka, India.

G. V. Srinivasa Reddy

Department of Irrigation and Drainage, College of Agril. Engg., UAS, Raichur, Karnataka, India.

A. G. Sreenivas

Department of Entomology, College of Agriculture., UAS, Raichur, Karnataka, India.

M. Y. Ajayakumar

ACRIP on Cotton, MARS, UAS, Raichur, Karnataka, India.

*Author to whom correspondence should be addressed.


Abstract

A study was conducted to predict the stream flow from a catchment area of Hidkal dam situated in Krishna basin of India. The SimHYD model was selected to setup the stream flow model under limited data conditions. Daily rainfall, potential evapotranspiration (PET) and observed discharges were used as input data to setup the model.  Sensitivity analysis was carried out to identify the more sensitive parameter and fixed final parameter values. A genetic algorithm was used for calibration and validation of the model with object function of Nash -Sutcliffe Equation (NSE). The performance of model during calibration and validation with monthly stream flow (m3/s) was found to be very good in terms of NSE, R2. The NSE and correlation coefficient were found to be 0.77 and 0.93 during calibration period and 0.90 and 0.95 during validation period respectively. The was a very good agreement between monthly observed and simulated stream flows of catchment area of Hidkal Dam. The NSE value of model during calibration period with daily runoff was found to be satisfactory at 0.51. It was observed that good agreement between observed and simulated daily runoff (in mm) with a correlation coefficient is 0.74. NSE and correlation coefficient during validation period using daily data are found to be 0.80 and 0.9 respectively. This study concluded that the SimHYD model can be used for assessment of stream flow with limited data.

Keywords: SIMHYD, stream flow, rainfall-runoff model, calibration and validation, sensitivity analysis


How to Cite

Rao, I. B., Nemichandrappa , M., Rao, K. V., Polisgowdar, B. S., Reddy, G. V. S., Sreenivas , A. G., & Ajayakumar , M. Y. (2024). Assessment of Stream Flow of Hidkal Dam Catchment Area in Krishna Basin of India Using SIMHYD Model. Journal of Geography, Environment and Earth Science International, 28(2), 11–26. https://doi.org/10.9734/jgeesi/2024/v28i2747

Downloads

Download data is not yet available.

References

Siriwardena L. Estimation of SIMHYD parameter values for application in ungauged catchments. MODSIM International Congress on Modelling and Simulation; 2005.

Zhang Y, Chiew FH. Relative merits of different methods for runoff predictions in ungauged catchments. Water Resource Research. 2009;45(7):1-13.

Srikanthan R, Chiew FHS, Harrold TI, Siriwardena L, Jones R. Simulation of climate change impact on runoff using rainfall scenarios that consider daily patterns of change from GCMs. In: MODSIM: International congress on modelling and simulation: Proceedings. Post, David A, ed. Modelling and Simulation Society of Australia and New Zealand, Canberra, 2003. pp. 154-159.

Yu B, Zhu Z. A comparative assessment of AWBM and SimHyd for forested watershed. Hydrological Science Journal, Special issues: Modelling Temporally variable Catchment. 2014;60(7-8):1200-1212. DOI: 10.1080/02626667.2014.961924

Chiew F, Zheng H, Potter N. Rainfall-Runoff modelling considerations to predict streamflow characteristics in ungauged catchments and under climate change. Water. 2018;10(10):1319. DOI: 10.3390/w10101319

Li C, Liu J, Yu F, Tian J, Wang Y, Qiu Q. Hydrological model calibration in data-limited catchments using non-continuous data series with different lengths, HIC. 13th International Conference on Hydroinformatics. 2018;3:1155-1161

Ramezani MR, Yu B, Tarakemehzadeh N. Satellite-derived spatiotemporal data on imperviousness for improved hydrological modelling of urbanised catchments. Journal of Hydrology. 2022;612:128101.

Bhasme P, Bhatia U. Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments. Journal of Hydrology. 2024; 628:130421.

Pai DS, Sridhar L, Rajeevan M, Sreejith OP, Satbhai NS, Mukhopadyay B. Development of a new high spatial resolution (0.25° × 0.25°) Long Period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam. 2013; 65(1):1-18.

Srivastava AK, Rajeevan M, Kshirsagar SR. Development of high resolution daily gridded temperature data set (1969-2005) for the Indian Region. Atmospheric Science Letters. 2009;10(4):249-254. DOI: 10.1002/asl.232

Goldberg DE, Holland JH. Genetic algorithms and machine learning. Machine Learning. 1988;3:95-99.

Chiew FHS, Siriwardena L. Estimation of SIMHYD parameter values for application in ungauged catchments MODSIM, International Congress on Modelling and Simulation; 2005.