Abstract
Understanding the projected changes in the mean and high flows remains a significant challenge due to uncertainty arising from global climate models (GCMs) and hydrological models. Moreover, the calibration approaches used for hydrological models can influence the climate change impact assessment. We use the combination of three hydrological models, four global climate models, and two RCPs (2.6 and 8.5) to analyze the projected changes in mean flow, high flow, and the frequency of high flow under the projected future climate in the Godavari River basin (GRB) until the gauge Tekra. The two evaluation approaches: a simple approach (TASK A) based on the calibration and validation at a single streamflow gauge station and a comprehensive approach (TASK B) based on multi-variable and multisite calibration and validation and trend analysis were employed to evaluate the hydrological models. The differences between the projected changes in mean and high flows calculated using models after TASK A and TASK B were estimated. Our results show that the differences can be up to 10–13% in mean annual flow and high flow, and up to 40% in high flow frequency. The comprehensively evaluated hydrological models were chosen for impact assessment, and they project increases in mean and high flows, and the frequency of high flow at all four gauge stations in the GRB. The projected increases are higher under RCP 8.5 and in the End century (2071–2100). Our results demonstrate the importance of the comprehensive evaluation of hydrological models in advance of climate change impact assessment.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aadhar S, Mishra V (2019) A substantial rise in the area and population affected by dryness in South Asia under 1.5, 2.0 and 2.5°C warmer worlds. Environ Res Lett. https://doi.org/10.1088/1748-9326/ab4862
Ali SA et al (2018) Projected increase in hydropower production in India under climate change. Sci Rep 8:12450. https://doi.org/10.1038/s41598-018-30489-4
Ali H et al (2019) Increased flood risk in Indian sub-continent under the warming climate. Weather Clim Extrem 25:100212. https://doi.org/10.1016/J.WACE.2019.100212
Allan RP, Soden BJ (2008) Atmospheric warming and the amplification of precipitation extremes. Science (80- ). https://doi.org/10.1126/science.1160787
Arnold JG, Engel BA, Srinivasan R (1993) Continuous time, grid cell watershed model, application of advanced information technologies. Effective management of natural resources. ASAE Publication, 04–93. American Society of Agricultural Engineers 267–278
Arnold JG et al (1998) Large area hydrologic modeling and assessment part I: model development. J Am Water Resour Assoc. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
Babar M, Kaplay RD (2018) Godavari River: geomorphology and socio-economic characteristics. In: The Indian Rivers. Springer Hydrogeology, Singapore, pp 319–337
Barnett TP et al (2005) Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438:303–309. https://doi.org/10.1038/nature04141
Brown C et al (2014) Analysing uncertainties in climate change impact assessment across sectors and scenarios. Clim Chang. https://doi.org/10.1007/s10584-014-1133-0
Calton B et al (2016) Water resource reanalysis v1: data access and model verification results. https://doi.org/10.5281/ZENODO.57760
Chaturvedi RK et al (2012) Multi-model climate change projections for India under representative concentration pathways. Curr Sci. https://doi.org/10.2307/24088836
Clark MP et al (2016) Characterizing uncertainty of the hydrologic impacts of climate change. Curr Clim Chang Rep 2:55–64. https://doi.org/10.1007/s40641-016-0034-x
Dee DP et al (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc. https://doi.org/10.1002/qj.828
Dottori F et al (2018) Increased human and economic losses from river flooding with anthropogenic warming. Nat Clim Chang 8:781–786. https://doi.org/10.1038/s41558-018-0257-z
Eisner S et al (2017) An ensemble analysis of climate change impacts on streamflow seasonality across 11 large river basins. Clim Chang. https://doi.org/10.1007/s10584-016-1844-5
Elliott J et al (2014) Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc Natl Acad Sci U S A. https://doi.org/10.1073/pnas.1222474110
Franchini M, Pacciani M (1991) Comparative analysis of several conceptual rainfall-runoff models. J Hydrol. https://doi.org/10.1016/0022-1694(91)90178-K
Frieler K et al (2017) Assessing the impacts of 1.5 °C global warming - simulation protocol of the inter-sectoral impact model Intercomparison project (ISIMIP2b). Geosci Model Dev. https://doi.org/10.5194/gmd-10-4321-2017
Garg S, Mishra V (2019) Role of extreme precipitation and initial hydrologic conditions on floods in Godavari river basin, India. Water Resour Res. https://doi.org/10.1029/2019WR025863
Ghosh S et al (2009) Trend analysis of Indian summer monsoon rainfall at different spatial scales. Atmos Sci Lett. https://doi.org/10.1002/asl
Gosain AK et al (2006) Climate change impact assessment on hydrology of Indian river basins. Curr Sci 90:346–353
Gosain AK et al (2011) Climate change impact assessment of water resources of India. Curr Sci 101:16
Goswami BN et al (2006) Increasing trend of extreme rain events over India in a warming environment. Science (80- ). https://doi.org/10.1126/science.1132027
Guhathakurta P et al (2011) Impact of climate change on extreme rainfall events and flood risk in India. J Earth Syst Sci. https://doi.org/10.1007/s12040-011-0082-5
Hempel S et al (2013) A trend-preserving bias correction – the ISI-MIP approach. Earth Syst Dyn. https://doi.org/10.5194/esd-4-219-2013
Immerzeel WW et al (2010) Climate change will affect the Asian water towers. Science (80- ). https://doi.org/10.1126/science.1183188
Kay AL et al (2009) Comparison of uncertainty sources for climate change impacts: flood frequency in England. Clim Chang. https://doi.org/10.1007/s10584-008-9471-4
Kling H et al (2012) Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. J Hydrol. https://doi.org/10.1016/j.jhydrol.2012.01.011
Krysanova V et al (1989) Simulation modelling of the coastal waters pollution from agricultural watershed. Ecol Model. https://doi.org/10.1016/0304-3800(89)90041-0
Krysanova V, Wechsung F, Arnold J, Srinivasan R, Williams J (2000) PIK Report Nr. 69 “SWIM (Soil and Water Integrated Model), User Manual”, Potsdam, p. 239
Krysanova V et al (2018) How the performance of hydrological models relates to credibility of projections under climate change. Hydrol Sci J. https://doi.org/10.1080/02626667.2018.1446214
Lange S (2018) Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset. Earth Syst Dyn. https://doi.org/10.5194/esd-9-627-2018
Liang X et al (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res. https://doi.org/10.1029/94JD00483
Liang X et al (1996) Surface soil moisture parameterization of the VIC-2L model: evaluation and modification. Glob Planet Chang. https://doi.org/10.1016/0921-8181(95)00046-1
Lohmann D et al (1996) A large-scale horizontal routing model to be coupled to land surface parameterization schemes. Tellus Ser A-Dyn Meteorol Oceanogr. https://doi.org/10.1034/j.1600-0870.1996.t01-3-00009.x
Maidment D (1993) GIS and hydrological modeling. In: Goodchild M, Parks B, Steyaert L (eds) Environmental Modeling within CIS. : University Press, Oxford, pp 147–167
Mall RK et al (2006) Impact of climate change on Indian agriculture: a review. Clim Chang 78:445–478
Mann HB (1945) Nonparametric tests against trend. Econometrica. https://doi.org/10.2307/1907187
Martens B et al (2017) GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci Model Dev. https://doi.org/10.5194/gmd-10-1903-2017
McSweeney CF, Jones RG (2016) How representative is the spread of climate projections from the 5 CMIP5 GCMs used in ISI-MIP? Clim Serv. https://doi.org/10.1016/j.cliser.2016.02.001
Meenu R et al (2013) Assessment of hydrologic impacts of climate change in Tunga-Bhadra river basin, India with HEC-HMS and SDSM. Hydrol Process. https://doi.org/10.1002/hyp.9220
Menon A et al (2013) Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models. Earth Syst Dyn. https://doi.org/10.5194/esd-4-287-2013
Merz R et al (2011) Time stability of catchment model parameters: implications for climate impact analyses. Water Resour Res. https://doi.org/10.1029/2010WR009505
Miralles DG et al (2011a) Magnitude and variability of land evaporation and its components at the global scale. Hydrol Earth Syst Sci. https://doi.org/10.5194/hess-15-967-2011
Miralles DG et al (2011b) Global land-surface evaporation estimated from satellite-based observations. Hydrol Earth Syst Sci. https://doi.org/10.5194/hess-15-453-2011
Mishra V, Lilhare R (2016) Hydrologic sensitivity of Indian sub-continental river basins to climate change. Glob Planet Chang. https://doi.org/10.1016/j.gloplacha.2016.01.003
Mishra V et al (2014) Reliability of regional and global climate models to simulate precipitation extremes over India. J Geophys Res. https://doi.org/10.1002/2014JD021636
Mishra V et al (2017) Multimodel assessment of sensitivity and uncertainty of evapotranspiration and a proxy for available water resources under climate change. Clim Chang 141:451–465. https://doi.org/10.1007/s10584-016-1886-8
Monteith JL (1965) Evaporation and environment. Symp Soc Exp Biol 19:205–224
Mu Q et al (2011) Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens Environ. https://doi.org/10.1016/j.rse.2011.02.019
Mukherjee S et al (2018) Increase in extreme precipitation events under anthropogenic warming in India. Weather Clim Extrem. https://doi.org/10.1016/j.wace.2018.03.005
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I - a discussion of principles. J Hydrol. https://doi.org/10.1016/0022-1694(70)90255-6
Neitsch SL, Arnold JG, Kiniry JR, Williams JR, King KW (2002) Soil and water assessment tool —theoretical documentation (ver. 2000). Texas Water Resour Inst:College Station, TX 458
Pall P et al (2007) Testing the Clausius-Clapeyron constraint on changes in extreme precipitation under CO2 warming. Clim Dyn. https://doi.org/10.1007/s00382-006-0180-2
Pechlivanidis IG et al (2016) Multi-basin modelling of future hydrological fluxes in the Indian subcontinent. Water (Switzerland). https://doi.org/10.3390/w8050177
Piontek F et al (2014) Multisectoral climate impact hotspots in a warming world. Proc Natl Acad Sci U S A. https://doi.org/10.1073/pnas.1222471110
Poulin A et al (2011) Uncertainty of hydrological modelling in climate change impact studies in a Canadian, snow-dominated river basin. J Hydrol. https://doi.org/10.1016/j.jhydrol.2011.08.057
Prein AF et al (2017) The future intensification of hourly precipitation extremes. Nat Clim Chang. https://doi.org/10.1038/nclimate3168
Rosenzweig C, Arnell NW, Ebi KL, Otze-Campen H, Raes F, Rapley C, Stafford Smith M, Cramer W, Frieler K, Reyer CPO, et al (2017) Assessing inter-sectoral climate change risks: The role of ISIMIP Assessing inter-sectoral climate change risks: The role of ISIMIP. Environ Res Lett 12
Schewe J et al (2014) Multimodel assessment of water scarcity under climate change. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.1222460110
Shah HL, Mishra V (2016) Hydrologic changes in Indian Subcontinental River basins (1901–2012). J Hydrometeorol 17:2667–2687. https://doi.org/10.1175/JHM-D-15-0231.1
Shah HL et al (2019) Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian Subcontinental River basins. J Geophys Res Atmos 124:12915–12936. https://doi.org/10.1029/2019JD031059
Sharma A, Wasko C, Lettenmaier DP (2018) If precipitation extremes are increasing, why aren’t floods? Water Resour Res 54(11):8545–8551. https://doi.org/10.1029/2018WR023749
Stackhouse PW, Jr., Gupta SK, Cox SJ, Zhang T, Mikovitz JC, Hinkelman LM (2011) The NASA/GEWEX surface radiation budget release 3.0: 24.5-year dataset. GEWEX News, No. 21 (1), International GEWEX Project Office, Silver Spring, MD, 10–12
Teng J et al (2012) Estimating the relative uncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff. J Hydrometeorol. https://doi.org/10.1175/JHM-D-11-058.1
Thompson JR et al (2013) Assessment of uncertainty in river flow projections for the Mekong River using multiple GCMs and hydrological models. J Hydrol. https://doi.org/10.1016/j.jhydrol.2013.01.029
Vetter T et al (2017) Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins. Clim Chang. https://doi.org/10.1007/s10584-016-1794-y
Vogel RM, Fennessey NM (1994) Flow-duration curves. I: New interpretation and confidence intervals. J Water Resour Plan Manag 120:485–504. https://doi.org/10.1061/(ASCE)0733-9496(1994)120:4(485)
Vogel RM, Fennessey NM (1995) Flow duration curves ii: a review of applications in water resources planning. J Am Water Resour Assoc 31:1029–1039. https://doi.org/10.1111/j.1752-1688.1995.tb03419.x
Wada Y, Bierkens MFP (2014) Sustainability of global water use: past reconstruction and future projections. Environ Res Lett. https://doi.org/10.1088/1748-9326/9/10/104003
Warszawski L, Frieler K, Huber V, Piontek F, Serdeczny O, Schewe J (2014) The inter-sectoral impact model intercomparison project (ISI-MIP): project framework. P Natl Acad Sci USA 111:3228–3232. https://doi.org/10.1073/pnas.1312330110
Weedon GP et al (2011) Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J Hydrometeorol. https://doi.org/10.1175/2011JHM1369.1
Wilby RL (2005) Uncertainty in water resource model parameters used for climate change impact assessment. Hydrol Process. https://doi.org/10.1002/hyp.5819
Wood AW et al (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Chang. https://doi.org/10.1023/B:CLIM.0000013685.99609.9e
Zhang W et al (2018) Reduced exposure to extreme precipitation from 0.5 °C less warming in global land monsoon regions. Nat Commun. https://doi.org/10.1038/s41467-018-05633-3
Acknowledgments
This work was supported by the Ministry of Earth Sciences and Ministry of Water Resources Government of India. The Federal Ministry for the Environment, Nature Conservation, and Nuclear Safety (BMU) support this initiative on the basis of a decision adopted by the German Bundestag. We appreciate the assistance from Stephanie Gleixner in preparation of supplemental figure(s).
Funding
The financial support was received for the project An Experimental Operational Hydrologic Modeling and Forecasting System for River Basin Hydrology and Extremes for India project, the East Africa Peru India Climate Capacities (EPICC) project, and the International Climate Initiative (IKI) project. The first author acknowledge the funding from Ministry of Water Resources, Ministry of Earth Sciences, and Ministry of Environment, Forest, and Climate Change, Government of India.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of a Special Issue on “How evaluation of hydrological models influences results of climate impact assessment”, edited by Valentina Krysanova, Fred Hattermann and Zbigniew Kundzewicz
Electronic supplementary material
ESM 1
(DOCX 2631 kb)
Rights and permissions
About this article
Cite this article
Mishra, V., Shah, H., López, M.R.R. et al. Does comprehensive evaluation of hydrological models influence projected changes of mean and high flows in the Godavari River basin?. Climatic Change 163, 1187–1205 (2020). https://doi.org/10.1007/s10584-020-02847-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10584-020-02847-7