Abstract
Projections of future streamflow extremes are subjected to various sources of uncertainties. Uncertainties arising from different stages in the modelling process of extremes contribute to the overall uncertainty in the final estimates. In this study, some important sources of uncertainties that occur in the modelling chain for projection of future extremes are analysed. The contribution of climate models, hydrological models, representative concentration pathways (RCPs) and the parameters of the extreme value distribution to the overall uncertainty in the streamflow return levels of the Chaliyar river basin in Kerala, India, was quantified using analysis of variance. 5, 10, 25, and 50-year return levels for the near (2021–2050) and far (2070–2099) future periods were computed. Results of the study indicate that the median values of return levels for the near (0.6–13.5%) and far (21.5–37.3%) future periods are higher than the corresponding values for the observed data. Also the uncertainty ranges of the return levels in the near (2–2.4 times) and far (3–3.7 times) future for different return periods are higher compared to that of the observed. The contribution to uncertainties in the return levels from climate models, parameters of the extreme value distribution employed and hydrological models is higher than that from the RCPs. The contribution of parameter uncertainty increases with the return period and that of the climate models reduces with the return period for the two future periods considered in this study, indicating that parameter uncertainty is an important source of uncertainty and has to be considered while predicting the future extremes. The contribution to uncertainty by the hydrological models and by the RCPs does not exhibit much variation for different return periods in both the future time periods considered. Understanding the uncertainties in future extremes would help to devise proper adaptation strategies to mitigate the probable impacts of climate change.
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Data availability
The climate model projections used in this study are from Coordinated Regional Climate Downscaling Experiment (CORDEX). These can be accessed through the climate data portal of Centre for Climate Change Research (CCCR), Indian Institute of Tropical Meteorology (IITM), Pune, India (http://cccr.tropmet.res.in).
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References
ASCE Task Committee on Definition of Criteria for Evaluation of Watershed Models of the Watershed Management Committee, Irrigation and Drainage Division (1993) Criteria for evaluation of watershed models. J Irrig Drainage Eng 119(3):429–442. https://doi.org/10.1061/(ASCE)0733-9437(1993)119:3(429)
Badjana HM, Renard B, Helmschrot J, Edjamé KS, Afouda A, Wala K (2017) Bayesian trend analysis in annual rainfall total, duration and maximum in the Kara River basin (West Africa). J Hydrol Reg Stud 13:255–273. https://doi.org/10.1016/j.ejrh.2017.08.009
Bastola S, Murphy C, Sweeney J (2011) The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments. Adv Water Resour 34(5):562–576. https://doi.org/10.1016/j.advwatres.2011.01.008
Bennett JC, Grose MR, Corney SP, White CJ, Holz GK, Katzfey JJ, Post DA, Bindoff NL (2014) Performance of an empirical bias-correction of a high-resolution climate dataset. Int J Climatol 34(7):2189–2204. https://doi.org/10.1002/joc.3830
Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6(3):279–298. https://doi.org/10.1002/hyp.3360060305
Burnash R, Ferral R, McGuire R (1973) A generalised streamflow simulation system – conceptual modelling for digital computers. National Weather Service, NOAA, and the State of California Tech. Rep. Joint Federal and State River Forecast Center.
Buytaert W, Célleri R, Timbe L (2009) Predicting climate change impacts on water resources in the tropical Andes: effects of GCM uncertainty. Geophys Res Lett 36(7):L07406. https://doi.org/10.1029/2008GL037048
Castillo E, Hadi AS (1994) Parameter and quantile estimation for the generalized extreme-value distribution. Environmetrics 5(4):417–432. https://doi.org/10.1002/env.3170050405
Chandra R, Saha U, Mujumdar PP (2015) Model and parameter uncertainty in IDF relationships under climate change. Adv Water Resour 79:127–139. https://doi.org/10.1016/j.advwatres.2015.02.011
Chen C, Haerter JO, Hagemann S, Piani C (2011) On the contribution of statistical bias correction to the uncertainty in the projected hydrological cycle. Geophys Res Lett 38(20):L20403. https://doi.org/10.1029/2011GL049318
Chen J, Brissette FP, Chaumont D, Braun M (2013) Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resour Res 49(7):4187-4205.852. https://doi.org/10.1002/wrcr.20331
Chilkoti V, Bolisetti T, Balachandar R (2020) Investigating the role of hydrological model parameter uncertainties in future streamflow projections. J Hydrol Eng 25(10):05020035. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001994
Collins M, Knutti R, Arblaster J, Dufresne JL, Fichefet T, Friedlingstein P, Gao X, Gutowski WJ, Johns T, Krinner G, Shongwe M, Booth BB (2013) Long-term climate change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner GK, Tignor MMB, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds.) Climate change 2013-the physical science basis: contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp 1029–1136
Das J, Treesa A, Umamahesh NV (2018) Modelling impacts of climate change on a river basin: analysis of uncertainty using REA & possibilistic approach. Water Resour Manag 32(15):4833–4834. https://doi.org/10.1007/s11269-018-2046-x
Das J, Umamahesh NV (2018a) Assessment of uncertainty in estimating future flood return levels under climate change. Nat Hazards 93(1):109–124. https://doi.org/10.1007/s11069-018-3291-2
Das J, Umamahesh NV (2018b) Spatio-temporal variation of water availability in a river basin under CORDEX simulated future projections. Water Resour Manag 32(4):1399–1419. https://doi.org/10.1007/s11269-017-1876-2
Deser C, Phillips A, Bourdette V, Teng H (2012) Uncertainty in climate change projections: the role of internal variability. Clim Dyn 38(3):527–546. https://doi.org/10.1007/s00382-010-0977-x
Duan Q, Sorooshian S, Gupta VK (1994) Optimal use of the SCE-UA global optimization method for calibrating watershed models. J Hydrol 158(3–4):265–284. https://doi.org/10.1016/0022-1694(94)90057-4
Duan QY, Gupta VK, Sorooshian S (1993) Shuffled complex evolution approach for effective and efficient global minimization. J Optim Theory Appl 76(3):501–521. https://doi.org/10.1007/BF00939380
Duda PB, Hummel PR, Donigian AS Jr, Imhoff JC (2012) BASINS/HSPF: model use, calibration, and validation. Trans ASABE 55(4):1523–1547. https://doi.org/10.13031/2013.42261
Enayati M, Bozorg-Haddad O, Bazrafshan J, Hejabi S, Chu X (2021) Bias correction capabilities of quantile mapping methods for rainfall and temperature variables. J Water Clim Chang 12(2):401–419. https://doi.org/10.2166/wcc.2020.261
Field CB, Barros V, Stocker TF et al. (2012) Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK, and New York, NY, USA, p. 582
Gao C, Booij MJ, Xu YP (2020) Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability. Hydrol Earth Syst Sci 24(6):3251–3269. https://doi.org/10.5194/hess-24-3251-2020
Gaur S, Bandyopadhyay A, Singh R (2020) Modelling potential impact of climate change and uncertainty on streamflow projections: a case study. J Water Clim Chang. https://doi.org/10.2166/wcc.2020.254
Gaur S, Bandyopadhyay A, Singh R (2021) From changing environment to changing extremes: exploring the future streamflow and associated uncertainties through integrated modelling system. Water Resour Manag 35(6):1889–1911. https://doi.org/10.1007/s11269-021-02817-3
Görgen K, Beersma J, Brahmer G, Buiteveld H, Carambia M, de Keizer O, Krahe P, Nilson E, Lammersen R, Perrin C, Volken D (2010) Assessment of climate change impacts on discharge in the Rhine River Basin: results of the RheinBlick2050 project. CHR report, I-23, pp 211
Gosling SN, Taylor RG, Arnell NW, Todd MC (2011) A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models. Hydrol Earth Syst Sci 15(1):279–294. https://doi.org/10.5194/hess-15-279-2011
Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Downscaling RCM precipitation to the station scale using statistical transformations–a comparison of methods. Hydrol Earth Syst Sci 16(9):3383–3390. https://doi.org/10.5194/hess-16-3383-2012
Huard D, Mailhot A, Duchesne S (2010) Bayesian estimation of intensity–duration–frequency curves and of the return period associated to a given rainfall event. Stoch Environ Res Risk Assess 24(3):337–347. https://doi.org/10.1007/s00477-009-0323-1
Hublart P, Ruelland D, Dezetter A, Jourde H (2015) Reducing structural uncertainty in conceptual hydrological modelling in the semi-arid Andes. Hydrol Earth Syst Sci 19(5):2295–2314. https://doi.org/10.5194/hess-19-2295-2015
Jaiswal RK, Ali S, Bharti B (2020) Comparative evaluation of conceptual and physical rainfall–runoff models. Appl Water Sci 10:1–14. https://doi.org/10.1007/s13201-019-1122-6
Jakeman AJ, Littlewood IG, Whitehead PG (1990) Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. J Hydrol 117(1–4):275–300. https://doi.org/10.1016/0022-1694(90)90097-H
Jenkinson AF (1955) The frequency distribution of the annual maximum (or minimum) values of meteorological elements. Q J R Meteorol Soc 81:158–171. https://doi.org/10.1002/qj.49708134804
Kay AL, Davies HN, Bell VA, Jones RG (2009) Comparison of uncertainty sources for climate change impacts: flood frequency in England. Clim Change 92(1):41–63. https://doi.org/10.1007/s10584-008-9471-4
Kundzewicz ZW (2008) Climate change impacts on the hydrological cycle. Ecohydrol Hydrobiol 8(2–4):195–203. https://doi.org/10.2478/v10104-009-0015-y
Kunnath-Poovakka A, Eldho TI (2019) A comparative study of conceptual rainfall-runoff models GR4J, AWBM and Sacramento at catchments in the upper Godavari river basin. India J Earth Syst Sci 128(2):1–15. https://doi.org/10.1007/s12040-018-1055-8
Le Treut H, Somerville R, Cubasch U, Ding Y, Mauritzen C, Mokssit A, Peterson T, Prather M (2007) Historical overview of climate change. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds.) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp 93–127
Levitus S, Antonov JI, Wang J, Delworth TL, Dixon KW, Broccoli AJ (2001) Anthropogenic warming of Earth’s climate system. Science 292(5515):267–270. https://doi.org/10.1126/science.1058154
Liersch S, Volk M (2008) A rainfall-runoff database to support flood risk assessment. Proceedings of 4th International Congress on Environmental Modelling and Software 494–502
Maraun D (2013) Bias correction, quantile mapping, and downscaling: revisiting the inflation issue. J Clim 26(6):2137–2143. https://doi.org/10.1175/JCLI-D-12-00821.1
Martins ES, Stedinger JR (2000) Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resour Res 36(3):737–744. https://doi.org/10.1029/1999WR900330
Maurer EP, Duffy PB (2005) Uncertainty in projections of streamflow changes due to climate change in California. Geophys Res Lett 32(3):L03704. https://doi.org/10.1029/2004GL021462
Milly PC, Dunne KA, Vecchia AV (2005) Global pattern of trends in streamflow and water availability in a changing climate. Nature 438(7066):347–350. https://doi.org/10.1038/nature04312
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900. https://doi.org/10.13031/2013.23153
Moriasi DN, Gitau MW, Pai N, Daggupati P (2015) Hydrologic and water quality models: performance measures and evaluation criteria. Trans ASABE 58(6):1763–1785. https://doi.org/10.13031/trans.58.10715
Muerth MJ, Gauvin St-Denis B, Ricard S, Velázquez JA, Schmid J, Minville M, Caya D, Chaumont D, Ludwig R, Turcotte R (2013) On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff. Hydrol Earth Syst Sci 17(3):1189–1204. https://doi.org/10.5194/hess-17-1189-2013
Mujumdar PP, Ghosh S (2008) Modeling GCM and scenario uncertainty using a possibilistic approach: application to the Mahanadi river, India. Water Resour Res 44(6):W06407. https://doi.org/10.1029/2007WR006137
Muleta MK (2012) Model performance sensitivity to objective function during automated calibrations. J Hydrol Eng 17(6):756–767. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000497
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290. https://doi.org/10.1016/0022-1694(70)90255-6
New M, Hulme M (2000) Representing uncertainty in climate change scenarios: a Monte-Carlo approach. Integr Assess 1(3):203–213. https://doi.org/10.1023/A:1019144202120
Pachauri RK, Allen MR, Barros VR et al. (2014) Climate change 2014: synthesis report. 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
Perrin C (2000) Vers une amélioration d’un modèle global pluie-débit au travers d’une approche comparative. PhD Thesis, INPG (Grenoble)/Cemagref (Antony), France, p 530
Perrin C, Michel C, Andréassian V (2003) Improvement of a parsimonious model for streamflow simulation. J Hydrol 279(1–4):275–289. https://doi.org/10.1016/S0022-1694(03)00225-7
Prudhomme C, Davies H (2009) Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate. Clim Change 93(1):197–222. https://doi.org/10.1007/s10584-008-9461-6
Raje D, Krishnan R (2012) Bayesian parameter uncertainty modeling in a macroscale hydrologic model and its impact on Indian river basin hydrology under climate change. Water Resour Res 48(8):W08522. https://doi.org/10.1029/2011WR011123
Raneesh KY, Thampi SG (2013) A simple semi-distributed hydrologic model to estimate groundwater recharge in a humid tropical basin. Water Resour Manag 27(5):1517–1532. https://doi.org/10.1007/s11269-012-0252-5
Riswana KP, Sithara Beegam CR (2019) Tale of tears: development and flood experience of Chaliyar grama panchayath, Malappuram. A Journal of Composition Theory 12(11):186–191
Stagl J, Mayr E, Koch H, Hattermann FF, Huang S (2014) Effects of climate change on the hydrological cycle in Central and Eastern Europe. In: Rannow S, Neubert M (eds.) Managing protected areas in Central and Eastern Europe under climate change. Springer, Dordrecht, pp 31–43
Semenov MA, Stratonovitch P (2010) Use of multi-model ensembles from global climate models for assessment of climate change impacts. Clim Res 41(1):1–14. https://doi.org/10.3354/cr00836
Sun H, Jiang T, Jing C, Su B, Wang G (2017) Uncertainty analysis of hydrological return period estimation, taking the upper Yangtze river as an example. Hydrol Earth Syst Sci Discuss p 1–26. https://doi.org/10.5194/hess-2016-566
Teng J, Potter NJ, Chiew FHS, Zhang L, Wang B, Vaze J, Evans JP (2015) How does bias correction of regional climate model precipitation affect modelled runoff? Hydrol Earth Syst Sci 19(2):711–728. https://doi.org/10.5194/hess-19-711-2015
Teng J, Vaze J, Chiew FH, Wang B, Perraud JM (2012) Estimating the relative uncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff. J Hydrometeorol 13(1):122–139. https://doi.org/10.1175/JHM-D-11-058.1
Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol 456:12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052
Thampi SG, Raneesh KY (2012) Impact of anticipated climate change on direct groundwater recharge in a humid tropical basin based on a simple conceptual model. Hydrol Process 26(11):1655–1671. https://doi.org/10.1002/hyp.8285
Wadhawan SK, Singh B, Ramesh MV (2020) Causative factors of landslides 2019: case study in Malappuram and Wayanad districts of Kerala. India Landslides 17(11):2689–2697. https://doi.org/10.1007/s10346-020-01520-5
Wijayarathne DB, Coulibaly P (2020) Identification of hydrological models for operational flood forecasting in St. John’s, Newfoundland. Canada. J Hydrol Reg Stud 27:100646. https://doi.org/10.1016/j.ejrh.2019.100646
Zhang L, Yuan F, Wang B, Ren L, Zhao C, Shi J, Liu Y, Jiang S, Yang X, Chen T, Liu S (2021) Quantifying uncertainty sources in extreme flow projections for three watersheds with different climate features in China. Atmos Res 249:105331. https://doi.org/10.1016/j.atmosres.2020.105331
Zhang S, Chen J, Gu L (2022) Overall uncertainty of climate change impacts on watershed hydrology in China. Int J Climatol 42(1):507–520. https://doi.org/10.1002/joc.7257
Zhang X, Xu YP, Fu G (2014) Uncertainties in SWAT extreme flow simulation under climate change. J Hydrol 515:205–222. https://doi.org/10.1016/j.jhydrol.2014.04.064
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The manuscript was prepared based on the discussion between Ms Ansa Thasneem S, Dr Chithra N R and Dr Santosh G Thampi. Ms Ansa Thasneem S carried out all the numerical simulations included in the manuscript. The manuscript was prepared by Ms Ansa Thasneem S and this was reviewed and corrected by Dr Santosh G Thampi and Dr Chithra N R.
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Ansa Thasneem, S., Chithra, N.R. & Thampi, S.G. Quantification of uncertainties in streamflow extremes in the Chaliyar river basin, India under climate change. Theor Appl Climatol 152, 435–453 (2023). https://doi.org/10.1007/s00704-023-04410-7
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DOI: https://doi.org/10.1007/s00704-023-04410-7