Analysis of Streamflow at 3, 5 and 7-Day Intervals at Four Gauging Stations of the Krishna River

Streamflow analysis plays a crucial role in understanding the availability and distribution of water within a river basin. Streamflow analysis helps us for predicting flood conditions and reducing the risks associated with heavy rainfall and extreme weather events. Additionally, streamflow analysis provides valuable insights into seasonal variations between monsoon and non-monsoon periods. Understanding daily variations in flow supports efficient utilization and conservation of water resources. The main objective of the current research works is to identify flood and drought conditions at four gauging stations of the Krishna River Basin and also an attempt is made to compute dependable flows at 3-day, 5-day, 7-day intervals. Based on the analysis we observed that very less streamflow discharge 0.0Mm3 at Sadalga gauging station, Samdoli gauging station, Terwad gauging station and Warunji gauging station during non-monsoon season and it is noticed that high discharge is occurred in the month of August as 1469.90 Mm3,1584.45 Mm3, 2394.20 Mm3 and 1030.85Mm3 respectively for 3-Daily. This research work is useful for proper understanding of streamflow behavior for the specific locations. However, analysis of real time streamflow data is used for various purposes such as different time water withdrawal, river flow forecasts, flood warnings, drought warnings and to understand the patterns of natural flow in the river.

Floods, Krishna River, Risk, Seasonal Variations, Streamflow.

1. Bent, G.C. and Steeves, P.A. 2006, A revised logistic regression equation and an automated procedure for mapping the probability of a stream flowing perennially in Massachusetts: U.S. Geological Survey Scientific Investigations Report 2006-5031, 107 p., 1 CD-ROM. [Google Scholar] [Crossref]

2. Bradford, M.J. and J.S. Heinnonen 2008. “Low Flows, Instream Flow Needs and Fish Ecology in Small Streams”. Canadian Water Resources Journal, 33(2): 165- 180. [Google Scholar] [Crossref]

3. Chi Yuan Miao , Wen Shi , Xun Hong Chen & Lin Yang (2012) Spatio-temporal variability of streamflow in the Yellow River: possible causes and implications, Hydrological Sciences Journal, 57:7, 1355-1367, DOI: 10.1080/02626667.2012.718077. https://doi.org/10.1080/02626667.2012.718077 [Google Scholar] [Crossref]

4. Chuck Kroll, Joana Luz, Brad Allen, and Richard M. Vogel, 2004, Developing a Watershed Characteristics Database to Improve Low Streamflow Prediction, Journal of Hydrologic Engineering, Volume 9, Issue 2, Feb 19, 2004, https://doi.org/10.1061/(ASCE)1084 0699(2004)9:2(116). [Google Scholar] [Crossref]

5. Jon E. Hortness, 2006, Estimating Low-Flow Frequency Statistics for Unregulated Streams in Idaho, prepared in cooperation with the Idaho, Department of Environmental Quality, page 1-31, https://pubs.usgs.gov/sir/2006/5035/pdf/sir20065035.pdf [Google Scholar] [Crossref]

6. K. Rashmi and Sreenivasulu Dandagala (2026), Analysis of Flood Risk Map using GIS at Selected Sites of Godavari River Basin: An Approach Towards Better Flood Management, Techno-Societal 2024 Proceedings of the 5th International Conference on Advanced Technologies for Societal Applications—Volume 1, Page No 241-251, https://doi.org/10.1007/978-3-032-13784-5_27 [Google Scholar] [Crossref]

7. M. Basha Mohiddin, P. Mallikarjuna, Sreenivasulu Dandagala (2018), "Application of Artificial Neural Network for streamflow forecasting-A Review" International Journal of Artificial Intelligent Systems and Machine Learning, Volume 10, Issue 2, Pp 25-29. February 2018, (Print and Online). [Google Scholar] [Crossref]

8. Ozgur kisi (2007), [23], “Streamflow Forecasting Using Different Artificial Neural Network Algorithms” Journal of Hydrologic Engineering, ASCE, September/October 2007, 12(5): 532-539 [Google Scholar] [Crossref]

9. Ozgur kisi 2005, [14]Daily River Flow Forecasting Using Artificial Neural Networks and Auto-Regressive Models, Turkish Journal of Engineering Environment Science, 29 (2005) , 9-20. [Google Scholar] [Crossref]

10. Searcy, J.K., 1959, Flow-duration curves: U.S. Geological Survey Water Supply Paper 1542, iv, 33 p. :ill. ;24 cm.; Reprinted 1963, https://doi.org/10.3133/wsp1542A. [Google Scholar] [Crossref]

11. Sreenivasulu Dandagala, Jhansy G and Jyosthna N (2014), Investigation on the Dependable Flows at polavaram site of Godavari River, Proceedings of National Conference on Advances in Civil Engineering 2014, 21st and 22nd August 2014, Department of Civil Engineering, Younus College of Engineering and Technology, Vadakkevila, Kollam, Kerala. [Google Scholar] [Crossref]

12. Sreenivasulu Dandagala, Jhansy G, Jyothsna N, Aswin S, Srinivasa Murthy(2014), [26] Trends in stream flow of the Somanpally gauging site at Godavari river basin, Journal of Chemical and Pharmaceutical Sciences ISSN: 0974-2115 JCHPS Special Issue 4: December 2014 www.jchps.com Page 65-67. [Google Scholar] [Crossref]

13. Sreenivasulu Dandagala, Nilav Kumar Karna and Nagaraj Gumageri (2010), Investigation on the Dependable Flows in River Section, International Conference on Earth Sciences and Engineering(ICEE)-2010, pp 44-50. [Google Scholar] [Crossref]

14. Tasker, G.D. 1987. “A Comparison of Methods of Estimating Low Flow Character- istics of Streams.” Water Resources Bulletin, 23(6): 349-370. [Google Scholar] [Crossref]

15. White field. H. and Cannona. J. Recent variations in climatic and hydrology in Canada. Canadian Water Resources Journal, 2000, 25, No. 1, 19-65. [Google Scholar] [Crossref]

16. Young, A.R., R. Grew and M.G.R. Holmes. 2003. “Low Flows 2000: A National Water Resources Assessment and Decision Support Tool.” Water Science & Technology, 48(10): 119-126. [Google Scholar] [Crossref]