Abstract
Climate change has long-term impacts on precipitation patterns, magnitude, and intensity, affecting regional water resources availability. Besides, understanding the interannual to decadal variations of streamflows in a river basin is paramount for watershed management, primarily extreme events such as floods and droughts. This study investigates impact of climate change in streamflows estimation for four sub-basins of the Mahanadi River, in India. The study includes three major components: (i) Historical trend analysis of hydroclimatic variables, using Mann–Kendall test; (ii) Statistical downscaling of GCM generated precipitation using change factor method and KnnCAD V4 stochastic weather generator; (iii) Dependable flow analysis of future streamflows predicted using Soil Water Assessment Tool (SWAT) model for various future GCM scenarios. It is observed that during the historical period, there is a decrease in number of rainy days and total annual precipitation in all sub-basins. However, the analysis also indicates an increase in flood intensity in two of the sub-basins. The decadal future precipitation has a marginal decrease in precipitation (up to 10%) for future scenarios; however, the precipitation events with high intensities increase. The results indicate that the magnitudes of 5 and 10% dependable flows are higher than the historically observed streamflows, for all future scenarios. This indicates a significant increase in extreme flood events in the basin. Further, only one of the sub-basins has shown an increase in water availability for agriculture and drinking water purposes (75 and 95% dependable flows) in the future. Understanding future flood events in the Mahanadi basin can help decision-makers to implement appropriate mitigation strategies.
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References
Anandhi A, Frei A, Pierson DC, Schneiderman EM, Zion MS, Lounsbury D, Matonse AH (2011) Examination of change factor methodologies for climate change impact assessment. Water Resour Res 47(3):W03501. https://doi.org/10.1029/2010WR009104
Apurv T, Mehrotra R, Sharma A, Goyal MK, Dutta S (2015) Impact of climate change on floods in the Brahmaputra basin using CMIP5 decadal predictions. J Hydrol 1(527):281–291
Bhatta B, Shrestha S, Shrestha PK, Talchabhadel R (2019) Evaluation and application of a SWAT model to assess the climate change impact on the hydrology of the Himalayan River Basin. CATENA 181:104082
Burn DH (1994) Hydrologic effects of climatic change in West Central Canada. J Hydrol 160(53):70
Burn DH, Cunderlik JM, Pietroniro A (2004) Hydrological trends and variability in the Liard river basin. Hydrol Sci J 49(1):53–67
Climate Change Cell (2009) Impact assessment of climate change and sea level rise on monsoon flooding. Climate Change Cell, Ministry of Environment. Component-4b: Comprehensive Disaster Programme, Ministry of Food and Disaster Man- agement, Dhaka, Bangladesh
Das S, Simonovic SP (2012) Assessment of uncertainty in flood flows under climate change impacts in the upper thames river basin, Canada. Br J Environ Clim Change 2(4):318–338
Gosain A, Rao S, Arora A (2011) Climate change impact assessment of water resources of India. Curr Sci 101:356–371
Gosain A, Rao S, Basuray D (2006) Climate change impact assessment on hydrology of Indian River Basins. Curr Sci 90
Hughes JP, Guttorp P (1994) A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resour Res 30(5):1535–1546
Humphrey GB, Gibbs MS, Dandy GC, Maier HR (2016) A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network. J Hydrol 1(540):623–640
IPCC (2014) Climate Change 2014: synthesis report. In: Core Writing Team RK, Pachauri, Meyer LA (eds) Contribution of working Groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. IPCC, Geneva, Switzerland, p 151
Khalilian S, Shahvari N (2019) A SWAT evaluation of the effects of climate
King L, Mcleod I, Simonovic S (2015) Improved weather generator algorithm for multisite simulation of precipitation and temperature. J Am Water Resour Assoc 7:1–16
Kumar A, Dudhia J, Rotunno R, Niyogi D, Mohanty UC (2005) Analysis of the 26 July 2005 heavy rain event over Mumbai, India using the Weather Research and Forecasting (WRF) model. Q J Roy Meteorol Soc 134(636):1897–1910. https://doi.org/10.1002/qj.325
Lee S, Yeo I, Sadeghi AM, McCarty GW, Hively WD, Lang MW, Sharifi A (2018) Comparative analyses of hydrological responses of two adjacent watersheds to climate variability and change using the SWAT model. Hydrol Earth Syst Sci 22:689–708. https://doi.org/10.5194/hess-22-689-2018
Masood M, Yeh PJ-F, Hanasaki N, Takeuchi K (2015) Model study of the impacts of future climate change on the hydrology of Ganges–Brahmaputra–Meghna basin. Hydrol Earth Syst Sci 19:747–770. https://doi.org/10.5194/hess-19-747-2015
Mishra V, Kumar D, Ganguly A, Sanjay J, Mujumdar M, Krishnan R, Shah R (2014) Reliability of regional and global climate models to simulate precipitation extremes over India. J Geophys Res Atmos 119(15):9301–9323
Mishra V, Lilhare R (2016) Hydrologic sensitivity of Indian sub-continental river basins to climate change. Glob Planet Change 139:78–96, ISSN 0921-8181, https://doi.org/10.1016/j.gloplacha.2016.01.003
Mujumdar PP (2011) Implications of climate change for water resources management (chapter 2), India infrastructure report, water: Policy and performance for sustainable developement, Oxford University Press, pp 18–28
Neitsch S et al (2005) Soil and water assessment tool theoretical documentation version 2005. Texas, USA
Panda DK, Kumar A, Ghosh S, Mohanty RK (2013) Streamflow trends in the Mahanadi River basin (India): Linkages to tropical climate variability. J Hydrol 495:135–149, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2013.04.054
Partal T, Kahya E (2006) Trend analysis in Turkish precipitation data. Hydrol Process 20:2011–2026
Planning Commission (2011) Report of working group on flood management and region specific issues for XII plan. Planning Commission, Government of India, New Delhi, India
Rajeevan M, Bhate J, Kale JD, Lal B (2006) High resolution daily gridded rainfall data for the Indian region: analysis of break and active monsoon spells. Curr Scie 91(3):296–306
Salas JD (1993) Analysis and modeling of hydrologic time series. In: Maidment DR (eds) Handbook of hydrology, 19.1 19.72. McGraw-Hill, New York, USA
Saleh DK, Kratzer CR, Green CH, Evans DG (2009) Using the soil and water assessment tool (SWAT) to simulate runoff in Mustang Creek Basin, California: U.S. Geo- logical Survey Scientific Investigations Report 2009–5031, p 28
Sharannya TM, Mudbhatkal A, Mahesha A (2018) Assessing climate change impacts on river hydrology–A case study in the Western Ghats of India. J Earth Syst Sci Indian Acad Sci 127:78. https://doi.org/10.1007/s12040-018-0979-3
Shiferaw H, Gebremedhin A, Gebretsadkan T, Zenebe A (2018) Modelling hydrological response under climate change scenarios using SWAT model: the case of Ilala watershed, Northern Ethiopia. Model Earth Syst Environ 4:437–449. https://doi.org/10.1007/s40808-018-0439-8
Shrestha S, Bhatta B, Shrestha M, Shrestha PK (2018) Integrated assessment of the climate and landuse change impact on hydrology and water quality in the Songkhram River Basin, Thailand. Sci Total Environ 643(2018):1610–1622
Shukla S, Lettenmaier 1007 DP (2013) Multi-RCM ensemble downscaling of NCEP CFS winter season forecasts: implications for seasonal hydrologic forecast skill. https://doi.org/10.1002/jgrd.50628
Singh V, Sharma A, Goyal MK (2017) Projection of hydro-climatological changes over eastern Himalayan catchment by the evaluation of RegCM4 RCM and CMIP5 GCM models. Hydrol Res 2017 Sep 14:nh2017193
Sood A, Muthuwatta L, McCartney M (2013) A SWAT evaluation of the effect of climate change on the hydrology of the Volta River basin. Water Int 38(3):297–311
Sudheer KP (2016) Impact of climate change on water resources in Madhya Pradesh—An assessment report, funded by Department of International Development (DFID)
Sun R, Zhang XQ, Sun Y et al (2013) SWAT-based streamflow estimation and its responses to climate change in the Kadongjia River watershed, southern Tibet. J Hydrometeorol 14(5):1571–1586
Sun S, Sun G, Mack EC, McNulty S, Caldwell PV, Duan K, Zhang Y (2016) Projecting water yield and ecosystem productivity across the United States by linking an ecohydrological model to WRF dynamically downscaled climate data. Hydrol Earth Syst Sci 20(2):935–952
Taylor CH, Loftis JC (1989) Testing for trend in lake and groundwater quality time series. Water Resour Bull 25(715):726
Trzaska S, Schnarr E (2014) A review of downscaling methods for climate change projections. United States Agency for International Development by Tetra Tech ARD. 2014:1–42
Von Storch H, Navarra A (1995) Analysis of climate variability applications of statistical techniques. Springer, New York, USA
Weibull W (1951) A statistical distribution function of wide applicability. J Appl Mech Trans ASME 18(3):293–297
Whitfield PH, Cannon AJ (2000) Recent variations in climate and hydrology in Canada (1)
Wilby RL, Dawson CW, Murphy C, O’Connor P, Hawkins E (2014) The statistical down—Scaling model—Decision Centric (SDSM-DC): Conceptual basis and applications. Clim Res 61:259–276. https://doi.org/10.3354/cr01254
World Bank (2008) Climate Change impacts in drought and flood affected areas: Case studies in India, Report No. 43946-IN
Xue Y, Janjic Z, Dudhia J, Vasic R, De Sales F (2014) A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability. Atmos Res 1:147
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This research did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Some data pertaining to the rainfall and river flow, which are classified data used in the study, were provided by a third party (Water Resources Department, Govt. of Odisha and Central Water Commission, Bhubaneswar, India). Direct request for these materials may be made to the provider.
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Conceptualisation: [Nayak P. C.]; Methodology: [Nayak P. C., Roshan Srivastav]; Formal analysis and investigation: [Nayak P. C., Poonam Wagh, Venkatesh B., Thomas T., Satyaji Rao Y.R.S.]; Writing - original draft preparation: [Nayak P. C., Poonam Wagh]; Supervision: [Nayak P. C., Roshan Srivastav].
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Nayak, P.C., Wagh, P., Venkatesh, B., Thomas, T., Srivastav, R. (2024). Statistical Downscaling of Precipitation for Mahanadi Basin in India—Prediction of Future Streamflows. In: Satheeshkumar, S., Thirukumaran, V., Karunanidhi, D. (eds) Modern River Science for Watershed Management. Water Science and Technology Library, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-031-54704-1_15
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