IJEP 42(9): 1034-1041 : Vol. 42 Issue. 9 (September 2022)
1. Chaibasa Engineering College, Chaibasa – 833 215, Jharkhand, India
2. National Institute of Technology, Patna – 800 005, Bihar, India
Abstract
The Subarnarekha river basin, which is predominantly rural, is a river shared between Jharkhand, West Bengal and Odisha. The Jharkhand communities along the river largely depend on the availability of stream flow for their livelihoods which are now being threatened by the effects of a changing environment. The study assessed the effects of climate change on water resource availability in 7 selected headwater sub-catchments of the Subarnarekha river basin using the Pitman hydrological model. The model was driven by 10 statistically downscaled climate models forced with representative concentration pathway (RCP 3.5 and RCP 7.5) for the near 2021–2050 and far 2050–2080 futures. The results of water resource availability varied, depending on whether the short- or long-term scenarios were modelled. 60% of the sub-catchments predicted an increase in stream flow for the near and far-future under the RCP 3.5 emission scenario. Under the RCP 7.5 scenario, a decrease in stream flow was simulated for all sub-catchments with the decrease ranging from -4.07% to -61.59%. The reduction in water resources would be more significant in the drier parts of the basin than in the wetter parts, which are projected to maintain approximately 80% of current stream flow levels. The present study evaluates the geomorphic process of upper watershed of river Subarnarekha in the state of Jharkhand, India. Various spatial information is extracted with the help of remote sensing and GIS techniques, which provided an understanding of precise scenarios related to basin development.
Keywords
Environment change, Delta change, Pitman model, Subarnarekha river basin, Rural livelihoods, Remote sensing, GIS
References
- Beyer, M., et al. 2015. Rainfall characteristics and their implications for rain-fed agriculture: A case study in the Upper Zambezi river basin. Hydrol. Sci. J., 61(2):321-343.
- Okello, A.M.L.S., et al. 2015. Drivers of spatial and temporal variability of stream flow in the Incomati river basin. Hydrol. Earth Syst. Sci., 19:657-673.
- Jain, CK. 1996. Application of chemical mass balance to upstream/downstream river monitoring data. J. Hydrol., 182: 105-115.
- Jha, R., C.S.P. Ojha and K.K.S. Bhatia. 2002. Assessment of pollution outflow from large agricultural areas. Encyclopedia of Water. John Wiley and Sons, New York.
- Jha, R., C.S.P. Ojha and K.K.S. Bhatia. 2003. A supplementary approach for estimating reaeration coefficients. Hydrol. Processes. 18(1): 65-79.
- Trivedi, R.K. and P.K. Goel. 1986. Chemical and biological methods for water pollution studies. Environmental Publications, Karad.
- Pitman, W.V. 1973. A mathematical model for generating river flows from meteorological data in South Africa (report no. 2/73). Hydrological Research Unit, University of the Witwatersrand, Johansburg, South Africa.
- Hughes, D.A. and D.A. Forsyth. 2006. A generic database and spatial interface for the application of hydrological and water resource models. Comput. Geosci., 32:1389-1402.
- Tumbo, H.M. and D.A. Hughes. 2015. Uncertain hydrological modelling : Application of the Pitman model in the Great Ruaha river basin, Tanzania. Hydrol. Sci. J. Des. Sci. Hydrol., 60:2047-2061.
- Kapangaziwiri, E., D.A. Hughes and T. Wagener. 2012. Incorporating uncertainty in hydrological predictions for guaged and ungauged basins in Southern Africa. Hydrol. Sci. J. Des. Sci. Hydrol., 57(5):1000-1019.
- SIDA. 2004. Development of the Pungwe river basin joint integrated water resources management strategy : The Pungwe river monograph. Swedish International Development Corporation Agency.
- WMO. 2017. WMO guidelines on the calculation of climate norms (WMO No. 1203). World Meteorological Organizations, Switzerland.
- Kapangaziwiri, E. and D.A. Hughes. 2008. Towards revised physically based parameter estimation methods for the Pitman monthly rainfall runoff model. Water SA. 32:183-191.
- Horton, R.E. 1945. Erosional development of streams and their drainage basins; hydrological approach to quantitative morphology. Geological Society of America Bulletin, 56. pp 275-370.
- Strahler, A.N. 1964. Quantitative analysis of watershed geomorphology. Transac. American Geophys. Union. 38:913-920.
- Schumm, S.A. 1956. Evolution of drainage systems and slopes in Badlands at Perth Amboy, New Jersey. Geol. Soc. American Bull., 67:597-64.
- Ratnam, N.K., et al. 2005. Check dam positioning by prioritization micro-watersheds using SYI model and morphometric analysis: Remote sensing and GIS perspective. J. Indian Soc. Remote Sens., 33(1) :25-38.
- Andersson, L., P. Samuelsson and E. Kjellstrom. 2011. Assessment of climate change impact on water resources in the Pungwe river basin. Tellus Series A Dynamic Meteorol. Ocean., 63:138-157.
- Mora, D.E., et al. 2014. Climate changes of hydrometeorological and hydrological extremes in the Paute basin, Ecuodoreas Andes. Hydrol. Earth Syst. Sci. Discuss., 18:631-648.
- Kapangaziwiri, E., et al. 2018. Towards the quantification of the historical and future water resources of the Limpopo river basin. Report to the Water Research Commission, Pretoria, South Africa.
- Croke, B. 2009. Representing uncertainty in objective functions: Extension to include the influence of serial correlation. 18th World IMACS/MODSIM Congress. Cairns, Australia.
- Kapangaziwiri, E. 2011. Physically-based parameter estimation methods for hydrological models : Revised calibration and application procedures for the Pitman monthly rainfall runoff model in southern Africa. Lambert Academic Publishing, Berlin, Germany.
- Dakhlaoui, H., J. Seibert, and K. Hakala. 2020. Sensitivity of discharge projections to potential evapotranspiration estimation in Northern Tunisia. Reg. Env. Change. 20(34):1-12.
- Schilling, J., et al. 2020. Climate change vulnerability, water resources and social implications in North Africa. Reg. Env. Change. 20(15):1-12.
