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Research Articles

Vol. 13 No. sp5 (2026): Recent Advances in Agriculture

Assessment of rainfall–streamflow relationship and its dynamics across the Mahanadi River Basin, India

DOI
https://doi.org/10.14719/pst.12432
Submitted
26 October 2025
Published
03-08-2026

Abstract

Understanding the rainfall-streamflow relationship is fundamental for effective water resource planning and agricultural management in river basins. This study presents a comprehensive, sub-catchment scale analysis of the rainfall-streamflow correlation, regression and runoff coefficients for 15 sub-catchments within the Mahanadi River Basin (MRB). High-resolution gridded rainfall data from the India Meteorological Department (IMD) and observed streamflow data from the Central Water Commission (CWC) were utilised from the year 1980 to 2024 to establish the relationships. The correlation analysis revealed a strong positive linear relationship between rainfall and streamflow across the basin, with a basin-average correlation coefficient (r) of 0.78. Sub-basin wise, the lower Mahanadi River Basin (LMRB) exhibited the strongest coupling (average r = 0.83), followed by the middle Mahanadi River Basin (MMRB, average r = 0.77) and upper Mahanadi River Basin (UMRB, average r = 0.75) sub-basins. The coefficient of determination (R²) followed a similar pattern. The estimated runoff coefficients (C), representing the fraction of rainfall converted into streamflow, showed significant spatial variability, ranging from 0.13 to 0.43 across sub-catchments. The upper and middle sub-basins showed lower runoff coefficients (UMRB: 0.22, MMRB: 0.32), while the lower sub-basin demonstrated a higher runoff coefficient of 0.35. These spatial patterns are attributed to the attenuating effects of major dams and reservoirs in the upper and middle reaches, higher forest cover promoting infiltration and contrasting topography. The flatter, deltaic lower basin, influenced by cumulative flow and higher water tables, generates runoff more efficiently. These findings are critical for designing sub-basin specific water management strategies. This study provides vital quantitative insights for optimising agricultural water use and implementing climate-resilient practices across the Mahanadi basin.

References

  1. 1. Wagener T, Sivapalan M, Troch PA, Woods R. Catchment classification and hydrologic similarity. Geography Compass. 2007;1(4):901–31. https://doi.org/10.1111/j.1749-8198.2007.00039.x
  2. 2. Merz R, Blöschl G. A regional analysis of event runoff coefficients with respect to climate and catchment characteristics in Austria. Water Resour Res. 2009;45(5). https://doi.org/10.1029/2008WR007163
  3. 3. Sivapalan M. Process, pattern and function: Elements of a unified theory of hydrology at the catchment scale. In: Encyclopedia of Hydrological Sciences. 2005. https://doi.org/10.1002/0470848944.hsa012
  4. 4. Central Water Commission. Government of India. 2019.
  5. 5. Sahu RK, Khare D. Spatial and temporal analysis of rainfall for 30 districts of a coastal state (Odisha) of India. Int J Geol Earth Environ Sci. 2015;5(1):40–53.
  6. 6. Ray SL, Sahu AP, Paul JC, Das DM, Raul SK, Kundu SK. Climate change impact on hydro-climatic fluxes in Kantamal Catchment of the Middle Mahanadi River Basin, India. J Agric Eng (India). 2024;61(6):890–909. https://doi.org/10.52151/jae2024616.1894
  7. 7. Sahu RT, Verma MK, Ahmad I. Impact of long-distance interaction indicator (monsoon indices) on spatio-temporal variability of precipitation over the Mahanadi River Basin. Water Resour Res. 2023;59(6):e2022WR033805. https://doi.org/10.1029/2022WR033805
  8. 8. Asokan SM, Dutta D. Analysis of water resources in the Mahanadi River Basin, India under projected climate conditions. Hydrol Process. 2008;22(18):3589–603. https://doi.org/10.1002/hyp.6962
  9. 9. Benchettouh A, Kouri L, Jebari S. Spatial estimation of soil erosion risk using RUSLE/GIS techniques and conservation practices suggested for reducing soil erosion in Wadi Mina watershed (northwest Algeria). Arab J Geosci. 2017;10(4):79. https://doi.org/10.1007/s12517-017-2875-6
  10. 10. Swain S, Mishra SK, Pandey A. A detailed assessment of meteorological drought characteristics using simplified rainfall index over Narmada River Basin, India. Environ Earth Sci. 2021;80(6):221. https://doi.org/10.1007/s12665-021-09512-x
  11. 11. Sen Z, Altunkayanak A. A comparative fuzzy logic approach to runoff coefficient and runoff estimation. Hydrol Process. 2006;20(9):1993–2009. https://doi.org/10.1002/hyp.5992
  12. 12. Kayitesi NM, Guzha AC, Mariethoz G. Impacts of land use land cover change and climate change on river hydro-morphology: A review of research studies in tropical regions. J Hydrol. 2022;615(Part A):128702. https://doi.org/10.1016/j.jhydrol.2022.128702
  13. 13. Das P, Behera MD, Patidar N, Sahoo B, Tripathi P, Behera PR, et al. Impact of LULC change on the runoff, base flow and evapotranspiration dynamics in eastern Indian river basins during 1985–2005 using variable infiltration capacity approach. J Earth Syst Sci. 2018;127(2):1–17. https://doi.org/10.1007/s12040-018-0921-8
  14. 14. Pai DS, Rajeevan M, Sreejith OP, Mukhopadhyay B, Satbha NS. 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. 2014;65(1):1–18. https://doi.org/10.54302/mausam.v65i1.851
  15. 15. Fraser C. Building multiple regression models. In: Business Statistics for Competitive Advantage with Excel and JMP. Cham: Springer Nature Switzerland; 2024. p. 179–200. https://doi.org/10.1007/978-3-031-42555-4_8
  16. 16. Mignon V. The multiple regression model. 2024. p. 105–70. https://doi.org/10.1007/978-3-031-52535-3_3
  17. 17. Samuel A, Joy KJ, Bhagat S. Integrated water management of the Mahanadi Basin. In: Water Conflicts in Odisha: A Compendium of Case Studies. 2017. p. 123–45.
  18. 18. Das DM, Nayak D, Sahoo BC, Raul SK, Panigrahi B, Choudhary KK. Identification of potential groundwater zones in rice-fallow areas within the Mahanadi River Basin, India, using GIS and the analytical hierarchy process. Environ Earth Sci. 2022;81(15):395. https://doi.org/10.1007/s12665-022-10517-3
  19. 19. Pathak H. Impact, adaptation and mitigation of climate change in Indian agriculture. Environ Monit Assess. 2023;195(1):10. https://doi.org/10.1007/s10661-022-10614-7
  20. 20. Hazra S, Ghosh A, Ghosh S, Pal I, Ghosh T. Assessing coastal vulnerability and governance in Mahanadi Delta, Odisha, India. Prog Disaster Sci. 2022;14:100223. https://doi.org/10.1016/j.pdisas.2022.100223
  21. 21. Naik PC. Hydro-geomorphology. In: Seawater Intrusion in the Coastal Alluvial Aquifers of the Mahanadi Delta. Cham: Springer International Publishing; 2017. p. 11–20. https://doi.org/10.1007/978-3-319-66511-5_2
  22. 22. Naha S, Rico-Ramirez MA, Rosolem R. Quantifying the impacts of land cover change on hydrological responses in the Mahanadi River Basin in India. Hydrol Earth Syst Sci. 2021;25(12):6339–57. https://doi.org/10.5194/hess-25-6339-2021
  23. 23. Nivesh S, Negi D, Kashyap PS, Aggarwal S, Singh B, Saran B, et al. Prediction of river discharge of Kesinga sub-catchment of Mahanadi Basin using machine learning approaches. Arab J Geosci. 2022;15(16):1398. https://doi.org/10.1007/s12517-022-10555-y

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