Climate Change Impact on Hydro-climatic Fluxes in Kantamal Catchmentof the Middle Mahanadi River Basin, India
DOI:
https://doi.org/10.52151/jae2024616.1894Keywords:
bias-correction, calibration, hydrological fluxes, Mann-Kendall test, sensitivity analysis, SWAT, trend analysis, water yieldAbstract
Climate change is now considered as a newly added threat for natural resource management, which has significant impact on agriculture and allied sectors. Climate change studies play a pivotal role in developing sustainable natural resource management strategies. The present study assesses the effect of climate change on hydro-climatic fluxes in Kantamal catchment of the Mahanadi River basin, India. Utilizing the modified Mann-Kendall test, analysis of long-term climatic variables revealed a decreasing trend in rainfall and increasing trend in temperature. Employing a bias-corrected, multi-model ensemble of three regional climate models (RegCM4-4, RCA4, REMO2009) under the Representative Concentration Pathways (RCPs) of RCP4.5 and RCP8.5 scenarios, a rise in the average maximum temperature of 0.86°C under RCP4.5 and 1.16°C under RCP8.5, as well as an increase in the average minimum temperature of 2.35°C under RCP4.5 and 2.89°C under RCP8.5, by 2099 were projected. Rainfall is projected to decrease by 29.53% (RCP4.5) and 24.34% (RCP8.5), with surface runoff decreasing by 13.91% (RCP4.5) and 9.94% (RCP8.5), actual evapotranspiration declining by 7.81% (RCP4.5) and 7.77% (RCP8.5), soil moisture reducing by 11.17% (RCP4.5) and 9.69% (RCP8.5), and water yield is projected to decline by 39.45% (RCP4.5) and 33.05% (RCP8.5) as compared to the baseline period. Water stress situation is anticipated in the catchment emphasizing the need for planning and management of water resources, and sustainable agriculture.
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Abbaspour K. C. (2015). SWAT-CUP: SWAT Calibration and Uncertainty Programs – A User Manual. Eawag: Swiss Federal Institute of Aquatic Science and Technology. Available at https://swat.tamu.edu/media/114860/usermanual_swatcup.pdf (accessed on 5 December 204).
Abbaspour, K. C., Johnson, C. A., & van Genuchten, M. T. (2004). Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal, 3(4), 1340-1352. https://doi.org/10.2113/3.4.1340
Abeysingha, N. S., Singh, M., Islam, A., Khanna, M., Sehgal, V. K., & Pathak, H. (2017). Impacts of climate change on stream flow in the Gomti River Basin of India. Journal of Agricultural Engineering (India), 54(4), 49-63. https://doi.org/10.52151/jae2017544.1639
Anjaneyulu, R., Swain, R., & Behera, M. D. (2023). Future projections of worst floods and dam break analysis in Mahanadi River Basin under CMIP6 climate change scenarios. Environmental Monitoring and Assessment, 195, 1173. https://doi.org/10.1007/s10661-023-11797-3
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., . . ., Jha, M. K. (2012). SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1491-1508. https://doi.org/10.13031/2013.42256
Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: Model development. Journal of the American Water Resources Association, 34(1), 73-89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
Bates, N. R., Mathis, J. T., & Cooper, L. W. (2009). Ocean acidification and biologically induced seasonality of carbonate mineral saturation states in the western Arctic Ocean. Journal of Geophysical Research, 114(C11), C11007. https://doi.org/10.1029/2008JC004862
Bhatta, B., Shrestha, S., Shrestha, P. K., & 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. https://doi.org/10.1016/j.catena.2019.104082
Bisht, D. S., Mohite, A. R., Jena, P. P., Khatun, A., Chatterjee, C., Raghuwanshi, N. S., . . ., Sahoo, B. (2020). Impact of climate change on streamflow regime of a large Indian river basin using a novel monthly hybrid bias correction technique and a conceptual modeling framework. Journal of Hydrology, 590, 125448. https://doi.org/10.1016/j.jhydrol.2020.125448
Christensen, J. H., Carter, T. R., Rummukainen, M., & Amanatidis, G. (2007). Evaluating the performance and utility of regional climate models: the PRUDENCE project. Climatic Change, 81(S1), 1-6. https://doi.org/10.1007/s10584-006-9211-6
Fowler, H. J., Blenkinsop, S., & Tebaldi, C. (2007). Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology, 27(12), 1547–1578. https://doi.org/10.1002/joc.1556
Gleick, P. H. (2000). The World’s Water 2000-2001. The biennial report on freshwater resources. Island Press, Washington, D.C., 315 p.
Grotch, S. L., & MacCracken, M. C. (1991). The use of general circulation models to predict regional climatic change. Journal of Climate, 4(3), 286-303. https://doi.org/10.1175/1520-0442(1991)004<0286:TUOGCM>2.0.CO;2
Gupta, H. V., Sorooshian, S., & Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2), 135-143. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135)
Gyamfi, C., Ndambuki, J. M., Anornu, G. K., & Kifanyi, G. E. (2017). Groundwater recharge modelling in a large scale basin: An example using the SWAT hydrologic model. Modeling Earth Systems and Environment, 3(4), 1361-1369. https://doi.org/10.1007/s40808-017-0383-z
Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1-4), 182-196. https://doi.org/10.1016/S0022-1694(97)00125-X
Hipel, K. W., & McLeod, A. I. (1994). Time Series Modelling of Water Resources and Environmental Systems. Elsevier, Amsterdam.
Ines, A. V. M., & Hansen, J. W. (2006). Bias correction of daily GCM rainfall for crop simulation studies. Agricultural and Forest Meteorology, 138(1-4), 44-53. https://doi.org/10.1016/j.agrformet.2006.03.009
IPCC. (2023). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland, pp. 35-115, https://doi.org/10.59327/IPCC/AR6-9789291691647
Islam, A., Sikka, A.K., Saha, B., & Sing, A. (2012). streamflow response to climate change in the Brahmani River Basin, India. Water Resources Management, 26, 1409-1424. https://doi.org/10.1007/s11269-011-9965-0
Jin, L., Whitehead, P. G., Rodda, H., Macadam, I., & Sarkar, S. (2018). Simulating climate change and socio-economic change impacts on flows and water quality in the Mahanadi River system, India. Science of The Total Environment, 637-638, 907-917, https://doi.org/10.1016/j.scitotenv.2018.04.349
Kendall, M. G. (1970). Rank Correlation Methods, 4th edition. Charles Griffin. High Wycombe, Bucks, 518p.
Kendall, M. G. (1975). Rank Correlation Methods. Griffin, London.
Knutti, R., Masson, D., & Gettelman, A. (2013). Climate model genealogy: Generation CMIP5 and how we got there. Geophysical Research Letters, 40(6), 1194-1199. https://doi.org/10.1002/grl.50256
Kumar, N., Tischbein, B., Kusche, J., Laux, P., Beg, M. K., & Bogardi, J. J. (2017). Impact of climate change on water resources of upper Kharun catchment in Chhattisgarh, India. Journal of Hydrology: Regional Studies, 13, 189-207. https://doi.org/10.1016/j.ejrh.2017.07.008
Kundzewicz, Z. W., Krysanova, V., Benestad, R. E., Hov, Ø., Piniewski, M., & Otto, I. M. (2018). Uncertainty in climate change impacts on water resources. Environmental Science & Policy, 79, 1-8. https://doi.org/10.1016/j.envsci.2017.10.008
Lilhare, R., & Mishra, V. (2014). GC13G-0747: Climate Change Impacts on Water Resources in Mahanadi River basin (India). In: AGU Fall Meeting Abstracts. AGU Fall Meeting, 15-19 December 2014, San Franscico.
Malhi, G. S., Kaur, M., & Kaushik, P. (2021). Impact of climate change on agriculture and its mitigation strategies: A review. Sustainability, 13(3), 1318. https://doi.org/10.3390/su13031318
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245-259. https://doi.org/10.2307/1907187
Moriasi, D. N., Arnold, J. G., Liew, M. W., Van, Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900. https://doi.org/10.13031/2013.23153
Naidu, C. V, Durgalakshmi, K., Krishna, K. M., Rao, S. R., Satyanarayana, G. C., Lakshminarayana, P., & Rao, L. M. (2009). Is summer monsoon rainfall decreasing over India in the global warming era? Journal of Geophysical Research, 114(D24), D24108. https://doi.org/10.1029/2008JD011288
Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I - A discussion of principles. Journal of Hydrology, 10(3), 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
Pandey, A., Bishal, K. C., Kalura, P., Chowdary, V. M., Jha, C. S., & Cerdà, A. (2021). A Soil Water Assessment Tool (SWAT) modeling approach to prioritize soil conservation management in river basin critical areas coupled with future climate scenario analysis. Air, Soil and Water Research, 14, 117862212110213. https://doi.org/10.1177/11786221211021395
Pettitt, A. N. (1979). A non-parametric approach to the change-point problem. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(2), 126-135. https://doi.org/10.2307/2346729
Piani, C., Haerter, J. O., & Coppola, E. (2010). Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology, 99(1-2), 187-192. https://doi.org/10.1007/s00704-009-0134-9
R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Rasool, T., & Kumar, R. (2019). Application of Soil and Water Assessment Tool for Runoff Simulation in a data scarce Himalayan watershed. Journal of Agricultural Engineering (India), 56(2), 136-146. https://doi.org/10.52151/jae2019562.1685
Saharia, A. M., & Sarma, A. K. (2018). Future climate change impact evaluation on hydrologic processes in the Bharalu and Basistha basins using SWAT model. Natural Hazards, 92(3), 1463-1488. https://doi.org/10.1007/s11069-018-3259-2
Salathé, E. P. (2003). Comparison of various precipitation downscaling methods for the simulation of streamflow in a rainshadow river basin. International Journal of Climatology, 23(8), 887-901. https://doi.org/10.1002/joc.922
Saraf, V. R., & Regulwar, D. G. (2018). Impact of climate change on runoff generation in the Upper Godavari River Basin, India. Journal of Hazardous, Toxic, and Radioactive Waste, 22(4), 04018021. https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000395
Schoenau, G. J., & Kehrig, R. A. (1990). Method for calculating degree-days to any base temperature. Energy and Buildings, 14(4), 299-302. https://doi.org/10.1016/0378-7788(90)90092-W
Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall’s Tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934
Shahvari, N., Khalilian, S., Mosavi, S. H., & Mortazavi, S. A. (2019). Assessing climate change impacts on water resources and crop yield: A case study of Varamin plain basin, Iran. Environmental Monitoring and Assessment, 191(3), 134. https://doi.org/10.1007/s10661-019-7266-x
Sharannya, T. M., Mudbhatkal, A., & Mahesha, A. (2018). Assessing climate change impacts on river hydrology - A case study in the Western Ghats of India. Journal of Earth System Science, 127(6), 78. https://doi.org/10.1007/s12040-018-0979-3
Smitha, P. S., Narasimhan, B., Sudheer, K. P., & Annamalai, H. (2018). An improved bias correction method of daily rainfall data using a sliding window technique for climate change impact assessment. Journal of Hydrology, 556, 100-118. https://doi.org/10.1016/j.jhydrol.2017.11.010
Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15(1), 72–101. https://doi.org/10.2307/1412159
Spearman, C. (2010). The proof and measurement of association between two things. International Journal of Epidemiology, 39(5), 1137–1150. https://doi.org/10.1093/ije/dyq191
Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456-457, 12-29. https://doi.org/10.1016/j.jhydrol.2012.05.052
Theil, H. (1950). A rank-invariant method of linear and polynomial regression analysis. Indagationes mathematicae, 12(85), 173.
Thom, H. C. S. (1958). A note on the gamma distribution. Monthly Weather Review, 86(4), 117-122. https://doi.org/10.1175/1520-0493(1958)086<0117:ANOTGD>2.0.CO;2
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., . . ., Rose, S. K. (2011). The representative concentration pathways: An overview. Climatic Change, 109(1-2), 5-31. https://doi.org/10.1007/s10584-011-0148-z
Vandana, K., Islam, A., Sarthi, P. P., Sikka, A. K., & Kapil, H. (2019). Assessment of potential impact of climate change on streamflow: a case study of the Brahmani River basin, India. Journal of Water and Climate Change, 10(3): 624–641. https://doi.org/10.2166/wcc.2018.129
Varughese, A., Praveena, K. K., Sruthakeerthi, P., Rachana, V. V., & Anjali. C. V (2022). Runoff prediction of Bharathapuzha River Basin using artificial neural network and SWAT model. Journal of Agricultural Engineering (India), 59(4), 404-416. https://doi.org/10.52151/jae2022594.1791
Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., . . ., Davies, P. M. (2010). Global threats to human water security and river biodiversity. Nature, 467(7315), 555-561. https://doi.org/10.1038/nature09440
White, K. L., & Chaubey, I. (2005). Sensitivity analysis, calibration, and validations for a multisite and multivariable SWAT Model. Journal of the American Water Resources Association, 41(5), 1077-1089. https://doi.org/10.1111/j.1752-1688.2005.tb03786.x
Wing, I. S., Cian, E. De, & Mistry, M. N. (2021). Global vulnerability of crop yields to climate change. Journal of Environmental Economics and Management, 109, 102462. https://doi.org/10.1016/j.jeem.2021.102462
World Meteorological Organization. (WMO). (2024). WMO Greenhouse Gas Bulletin- The State of Greenhouse Gases in the Atmosphere Based on Global Observations through 2023. WMO Greenhouse Gas Bulletin No. 20, World Meteorological Organization, Geneva, 11 p. Available at: https://library.wmo.int/idurl/4/69057 (accessed on 01 December 204).





