Uncertainties in river discharge simulations of the upper Indus basin in the Western Himalayas

Abstract

This study focuses on understanding and quantifying uncertainties in simulating river discharge in the Upper Indus Basin (UIB) of the Western Himalayas using a macro-scale semi-distributed hydrology model forced with multiple observed precipitation datasets and reanalysis products of near-surface wind-speed, maximum and minimum temperature during 2010–2012. We performed a suite of numerical simulations using a high-resolution setup of the variable infiltration capacity (VIC) hydrology model for the UIB. This model takes into account the balance of both water and surface energy budgets within each grid cell and incorporates sub-grid variability of topography to represent the effects of orographic precipitation and temperature lapse rate essential for hydrological modelling in the complex Himalayan terrain. While river discharges over non-mountainous basins are known to be generally sensitive to precipitation variations, it is noted that both precipitation and snowmelt processes critically influence seasonal river discharge in the UIB during the northern summer through surface temperature and wind-speed variations. Our study found that a marginal difference in temperature forcing can create large difference in snowmelt over UIB during summer season, which in turn increases the uncertainty in the summer monsoon river discharge. This analysis highlights the equally important need for the incorporation of realistic temperature data as that of precipitation product for the better simulation of land surface processes during various seasons, especially during summer, over snow covered UIB. Further analysis of daily simulations of the VIC model during 2010–2012 indicates that low and medium intensity river discharges tend to be associated with relatively lower spread among the ensemble members, as compared to the high intensity discharges which exhibit large ensemble spread. In particular, we noted a large increase in the spread of high flow simulations over the UIB during the flood episodes in the summer of 2010, arising from uncertainties in the precipitation forcing across multiple datasets. Our results emphasize the need for improved representation of precipitation and hydrological processes over the Himalayan region in weather and climate models for better management of water resources and flood forecasting in the UIB region under a changing climate.

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References

  1. Abdulla F A, Lettenmaier D P, Wood E and Smith J A 1996 Application of a macroscale hydrologic model to estimate the water balance of the Arkansas-Red River Basin; J. Geophys. Res. 101(D3) 7449–7459, https://doi.org/10.1029/95JD02416.

    Article  Google Scholar 

  2. Adams T E 2019 Water resources forecasting within the Indus River Basin: A call for comprehensive modeling; In: Indus River Basin: Water security and sustainability (eds) Khan S I and Adams T E, Elsevier, pp. 267–307, https://doi.org/10.1016/B978-0-12-812782-7.00013-8.

  3. Andreadis K M, Storck P and Lettenmaier D P 2009 Modeling snow accumulation and ablation processes in forested environments; Water Resour. Res. 45(5) https://doi.org/10.1029/2008WR007042.

  4. Annamalai H, Hamilton K and Sperber K R 2007 South Asian Summer Monsoon and its relationship with ENSO in the IPCC AR4 simulations; J. Climate. 20 1071–1092, https://doi.org/10.1175/JCLI4035.1.

    Article  Google Scholar 

  5. Akhtar S 2011 The south Asiatic monsoon and flood hazards in the Indus river basin, Pakistan; J. Basic Appl. Sci. 7(2), https://doi.org/10.6000/1927-5129.2011.07.02.05.

  6. Arnell N W 1999a A simple water balance model for the simulation of stream-flow over a large geographic domain; J. Hydrol. 217(3) 314–335, https://doi.org/10.1016/S0022-1694(99)00023-2.

    Article  Google Scholar 

  7. Arnell N W 1999b Climate change and global water resources; Global Environ. Chang. 9(S1) S31–S49, https://doi.org/10.1016/S0959-3780(99)00017-5.

    Article  Google Scholar 

  8. Bao X and Zhang F 2019 How accurate are modern atmospheric reanalyses for the data-sparse Tibetan Plateau region?; J. Climate. 32(21) 7153–7172, https://doi.org/10.1175/JCLI-D-18-0705.1.

    Article  Google Scholar 

  9. Bohn T J, Livneh B, Oyler J W, Running S W, Nijssen B and Lettenmaier D P 2013 Global evaluation of MTCLIM and related algorithms for forcing of ecological and hydrological models; Agric. For. Meteorol. 176 38–49, https://doi.org/10.1016/j.agrformet.2013.03.003.

    Article  Google Scholar 

  10. Butts M B, Payne J T, Kristensen M and Madsen H 2004 An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation; J. Hydrol. 298(1) 242–266, https://doi.org/10.1016/j.jhydrol.2004.03.042.

    Article  Google Scholar 

  11. Bookhagen B and Burbank D W 2010 Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge; J. Geophys. Res. 115(F3), https://doi.org/10.1029/2009JF001426.

  12. Cannon F, Carvalho L M V, Jones C and Norris J 2016 Winter westerly disturbance dynamics and precipitation in the Western Himalaya and Karakoram: A wave-tracking approach; Theor. Appl. Climatol. 125 27–44, https://doi.org/10.1007/s00704-015-1489-8.

    Article  Google Scholar 

  13. Chen M, Shi W, Xie P, Silva V B S, Kousky V E, Wayne R, Higgins and Janowiak J E 2008 Assessing objective techniques for gauge-based analyses of global daily precipitation; J. Geophys. Res. 113 D04110, https://doi.org/10.1029/2007JD009132.

    Article  Google Scholar 

  14. Dadic R, Mott R, Lehning M and Burlando P 2010 Wind influence on snow depth distribution and accumulation over glaciers; J. Geophys. Res. 115(F1) F01012, https://doi.org/10.1029/2009JF001261.

    Article  Google Scholar 

  15. Dahri Z H, Ludwig F, Moors E, Ahmad B, Khan A and Kabat P 2016 An appraisal of precipitation distribution in the high-altitude catchments of the Indus basin; Sci. Total Environ. 548 289–306, https://doi.org/10.1016/j.scitotenv.2016.01.001.

    Article  Google Scholar 

  16. Danielson J J and Gesch D B 2011 Global multi-resolution terrain elevation data 2010 (GMTED2010); Technical report, US Geological Survey, https://doi.org/10.3133/ofr20111073.

    Article  Google Scholar 

  17. Dee D P, Uppala S M, Simmons A J, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M A, Balsamo G, Bauer D P and Bechtold P 2011 The ERA-Interim reanalysis: Configuration and performance of the data assimilation system; Quart. J. Roy. Meteorol. Soc. 137(656) 553–597, https://doi.org/10.1002/qj.828.

    Article  Google Scholar 

  18. Dimri A P, Niyogi D, Barros A, Ridley J, Mohanty U C, Yasunari and Sikka D R 2015 Western disturbances: A review; Rev. Geophys. 53(2) 225–246, https://doi.org/10.1002/2014RG000460.

    Article  Google Scholar 

  19. Fan F, Mann M E, Lee S and Evans J L 2010 Observed and modelled changes in the South Asian summer monsoon over the historical period; J. Clim. 23(19) 5193–5205, https://doi.org/10.1175/2010JCLI3374.1.

    Article  Google Scholar 

  20. Ferraro R R 1997 SSM/I derived global rainfall estimates for climatological applications; J. Geophys. Res. 102(D14) 16,715–16,735, https://doi.org/10.1029/97JD01210.

    Article  Google Scholar 

  21. Ferraro R R, Weng F, Grody N C and Zhao L 2000 Precipitation characteristics over land from the NOAA-15 AMSU sensor; Geophys. Res. Lett. 27(17) 2669–2672, https://doi.org/10.1029/2000GL011665.

    Article  Google Scholar 

  22. Franchini M and Pacciani M 1991 Comparative analysis of several conceptual rainfall-runoff models; J. Hydrol. 122(1–4) 161–219, https://doi.org/10.1016/0022-1694(91)90178-K.

    Article  Google Scholar 

  23. Funk C C, Peterson P J, Landsfeld M, Pedreros D H, Verdin J P, Rowland J D, Romero B E, Husak G J, Michaelsen J C and Verdin A P 2014 A quasi-global precipitation time series for drought monitoring; US Geological Survey Data Series 832 4, https://dx.doi.org/10.3133/ds832.

    Article  Google Scholar 

  24. Gaurav K, Sinha R and Panda P K 2011 The Indus flood of 2010 in Pakistan: A perspective analysis using remote sensing data; Nat. Hazards 52(1) 1815–1826, https://doi.org/10.1007/s11069-011-9869-6.

    Article  Google Scholar 

  25. Gelaro R, McCarty W, Suárez M J, Todling R, Molod A, Takacs L, Randles C L, Darmenov A, Bosilovich M G, Reichle R, Wargan K, Coy L, Cullather R, Draper C, Akella S, Buchard V, Conaty A, Silva A M, Gu W, Kim G-K, Koster R, Lucchesi R, Merkova D, Nielsen J E, Partyka G, Pawson S, Putman W, Rienecker M, Schubert S D, Sienkiewicz M and Zhao B 2017 The modern-era retrospective analysis for research and applications, version 2 (MERRA-2); J. Clim. 30(14) 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    Article  Google Scholar 

  26. Georgakakos K, Seo D-J, Gupta H, Schaake J and Butts M B 2004 Towards the characterization of streamflow simulation uncertainty through multimodel ensembles; J. Hydrol. 298(1) 222–241, https://doi.org/10.1016/j.jhydrol.2004.03.037.

    Article  Google Scholar 

  27. Ghosh S 2010 SVM-PGSL coupled approach for statistical downscaling to predict rainfall from GCM output; J. Geophys. Res. 115(D22), https://doi.org/10.1029/2009JD013548.

  28. Ghosh S and Mujumdar P P 2008 Statistical downscaling of GCM simulations to Streamflow using relevance vector machine; Adv. Water. Resour. 31(1) 132–146, https://doi.org/10.1016/j.advwatres.2007.07.005.

    Article  Google Scholar 

  29. Hansen M, DeFries R, Townshend J R and Sohlberg R 2000 Global land cover classification at 1 km spatial resolution using a classification tree approach; J. Remote Sens. 21(6–7) 1331–1364, https://doi.org/10.1080/014311600210209.

    Article  Google Scholar 

  30. Hashmi H N, Siddiqui Q T M, Ghumman A R and Kamal M A 2012 A critical analysis of 2010 floods in Pakistan; Afr. J. Agric. Res. 7(7) 1054–1067, https://academicjournals.org/journal/AJAR/article-abstract/ED4193144814.

  31. Hasson S, Lucarini V and Pascale S 2013 Hydrological cycle over South and Southeast Asian river basins as simulated by PCMDI/CMIP3 experiments; Earth Syst. Dynam. 4(2) 199–217, https://doi.org/10.5194/esd-4-199-2013.

    Article  Google Scholar 

  32. Hasson S, Pascale S, Lucarini V and Böhner J 2016 Seasonal cycle of precipitation over major river basins in South and Southeast Asia: A review of the CMIP5 climate models data for present climate and future climate projections; Atmos. Res. 180 42–63, https://doi.org/10.1016/j.atmosres.2016.05.008.

    Article  Google Scholar 

  33. Hersbach H, Bell B, Berrisford P, Horányi A, Joaquin M S, Nicolas J, Radu R, Schepers D, Simmons A, Soci C and Dee D 2019 Global reanalysis: Goodbye ERA-Interim, hello ERA5; ECMWF Newsletter 159 17–24, https://doi.org/10.21957/vf291hehd7.

  34. Hong C-C, Hsu H-H, Lin N-H and Chiu H 2011 Roles of European blocking and tropical–extratropical interaction in the 2010 Pakistan flooding; Geophys. Res. Lett. 38(13) L13806, https://doi.org/10.1029/2011GL047583.

    Article  Google Scholar 

  35. Houze Jr R A, Rasmussen K L, Medina S, Brodzik S R and Romatschke U 2011 Anomalous atmospheric events leading to the summer 2010 floods in Pakistan; Bull. Am. Meteorol. Soc. 92(3) 291–298, https://doi.org/10.1175/2010BAMS3173.1.

    Article  Google Scholar 

  36. Hunt K M, Turner A G and Shaffrey L C 2019 Representation of western disturbances in CMIP5 models; J. Clim. 32(7) 1997–2011, https://doi.org/10.1175/JCLI-D-18-0420.1.

    Article  Google Scholar 

  37. Hussain S, Song X, Ren G, Hussain I, Han D and Zaman M H 2017 Evaluation of gridded precipitation data in the Hindu Kush–Karakoram–Himalaya mountainous area; Hydrol. Sci. J. 62(14) 2393–2405, https://doi.org/10.1080/02626667.2017.1384548.

    Article  Google Scholar 

  38. Huffman G J, Adler R F, Morrissey M M, Bolvin D T, Curtis S, Joyce R, McGavock B and Susskind J 2001 Global precipitation at one-degree daily resolution from multisatellite observations; J. Hydrometeorol. 2(1) 36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.

    Article  Google Scholar 

  39. Huffman G J, Bolvin D T, Nelkin E J, Wolf D B, Adler R F, Gu G, Hong Y, Bowman K and Stocker E F 2007 The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales; J. Hydrometeorol. 8(1) 38–55, https://doi.org/10.1175/JHM560.1.

    Article  Google Scholar 

  40. Immerzeel W W, Van Beek L P and Bierkens M F 2010 Climate change will affect the Asian water towers; Science 328(5984) 1382–1385, https://doi.org/10.1126/science.1183188.

    Article  Google Scholar 

  41. Joseph J, Ghosh S, Pathak A and Sahai A K 2018 Hydrologic Impacts of climate change: Comparisons between hydrological parameter uncertainty and climate model uncertainty; J. Hydrol. 566 1–22, https://doi.org/10.1016/j.jhydrol.2018.08.080.

    Article  Google Scholar 

  42. Joyce R J, John E J, Phillip A A and Pingping X 2004 CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution; J. Hydrometeorol. 5 487–503, https://doi.org/10.1175/1525-7541(2004)005%3c0487:CAMTPG%3e2.0.CO;2

    Article  Google Scholar 

  43. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K C, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R and Joseph D 1996 The NCEP/NCAR 40-year reanalysis project; Bull. Am. Meteor. Soc. 77 437–470, https://doi.org/10.1175/1520-0477(1996)077%3c0437:TNYRP%3e2.0.CO;2.

    Article  Google Scholar 

  44. Khan S I and Adams T E (eds) 2019 Indus River Basin: Water security and sustainability; Elsevier, https://www.elsevier.com/books/indus-river-basin/khan/978-0-12-812782-7.

  45. Kim J, Sanjay J, Mattmann C, Boustani M, Ramarao M, Krishnan R and Waliser D 2015 Uncertainties in estimating spatial and interannual variations in precipitation climatology in the India–Tibet region from multiple gridded precipitation datasets; Int. J. Climatol. 35(15) 4557–4573, https://doi.org/10.1002/joc.4306.

    Article  Google Scholar 

  46. Kimball J S, Thornton P E, White M A and Running S W 1997 Simulating forest productivity and surface-atmosphere carbon exchange in the BOREAS study region; Tree Physiol. 17(8–9) 589–599, https://doi.org/10.1093/treephys/17.8-9.589.

    Article  Google Scholar 

  47. Kitoh A, Yukimoto S, Noda A and Motoi T 1997 Simulated changes in the Asian summer monsoon at times of increased atmospheric CO2; J. Meteor. Soc. Japan Ser II 75(6) 1019–1031, https://doi.org/10.2151/jmsj1965.75.6_1019.

    Article  Google Scholar 

  48. Kobold M and Sušelj K 2005 Precipitation forecasts and their uncertainty as input into hydrological models; Hydrol. Earth Syst. Sci. 9(4) 322–332, https://doi.org/10.5194/hess-9-322-2005.

    Article  Google Scholar 

  49. Kobayashi S, Ota Y, Harada Y, Ebita A, Moriya M, Onoda H, Onogi K, Kamahori H, Kobayashi C, Endo H, Miyaoka K and Takahashi K 2015 The JRA-55 reanalysis: General specifications and basic characteristics; J. Meteor. Soc. Japan Ser. II. 93(1) 5–48, https://doi.org/10.2151/jmsj.2015-001.

    Article  Google Scholar 

  50. Kripalani R, Oh J, Kulkarni A, Sabade Sand Chaudhari H 2007 South Asian summer monsoon precipitation variability: Coupled climate model simulations and projections under IPCC AR4; Theor. Appl. Climatol. 90(3–4) 133–159, https://doi.org/10.1007/s00704-006-0282-0.

    Article  Google Scholar 

  51. Krishnan R, Sabin T, Ayantika D, Kitoh A, Sugi M, Murakami H, Turner A, Slingo Jand Rajendran K 2013 Will the South Asian monsoon overturning circulation stabilize any further? Clim. Dyn. 40(1–2) 187–211, https://doi.org/10.1007/s00382-012-1317-0.

    Article  Google Scholar 

  52. Krishnan R, Sabin T P, Madhura R K, Vellore R K, Mujumda M, Sanjay J, Nayak S and Rajeevan M 2019a Non-monsoonal precipitation response over the Western Himalayas to climate change; Clim. Dyn. 52(7–8) 4091–4109, https://doi.org/10.1007/s00382-018-4357-2.

    Article  Google Scholar 

  53. Krishnan R, Shrestha A B, Ren G, Rajbhandari R, Saeed S, Sanjay J, Syed M A, Vellore R, Xu Y, You Q and Ren Y 2019b Unravelling climate change in the Hindu Kush Himalaya: Rapid warming in the mountains and increasing extremes; In: The Hindu Kush Himalaya Assessment (eds) Wester P, Mishra A, Mukherji A and Shrestha A B, Springer, Cham, pp. 57–97, https://doi.org/10.1007/978-3-319-92288-1_3.

  54. Kumar K R, Sahai A K, Kumar K K, Patwardhan S K, Mishra P K, Revadekar J V, Kamala K and Pant G B 2006 High-resolution climate change scenarios for India for the 21st century; Curr. Sci. 90(3) 334–345, http://repository.ias.ac.in/67506/1/67506.pdf.

  55. Latif M, Syed F and Hannachi A 2016 Rainfall trends in the South Asian summer monsoon and its related large-scale dynamics with focus over Pakistan; Clim. Dyn. 48 3565–3581, https://doi.org/10.1007/s00382-016-3284-3.

    Article  Google Scholar 

  56. Lau W K and Kim K-M 2012 The 2010 Pakistan flood and Russian heat wave: Teleconnection of hydrometeorological extremes; J. Hydrometeorol. 13(1) 392–403, https://doi.org/10.1175/JHM-D-11-016.1.

    Article  Google Scholar 

  57. Liang X, Lettenmaier D P, Wood E F and Burges S J 1994 A simple hydrologically based model of land surface water and energy fluxes for general circulation models; J. Geophys. Res. 99(D7) 14,415–14,428, https://doi.org/10.1029/94JD00483.

    Article  Google Scholar 

  58. Liang X, Wood E F and Lettenmaier D P 1996 Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification. Global Planet. Change 13(1–4) 195–206, https://doi.org/10.1016/0921-8181(95)00046-1.

    Article  Google Scholar 

  59. Liang S, Zhao X, Liu S, Yuan W, Cheng X, Xiao Z, Zhang X, Liu Q, Cheng J, Tang H and Qu Y 2013 A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies; Int. J. Digit. Earth. 6 5–33, https://doi.org/10.1080/17538947.2013.805262.

    Article  Google Scholar 

  60. Liu Q, Wang L, Qu Y, Liu N, Liu S, Tang H, Liang S 2013 Preliminary evaluation of the long-term GLASS albedo product; Int. J. Digit. Earth 6 69–95, https://doi.org/10.1080/17538947.2013.804601.

    Article  Google Scholar 

  61. Liu X and Yan L 2017 Elevation-dependent climate change in the Tibetan Plateau; In: Oxford Research Encyclopedia of climate science, Oxford University Press, USA, pp. 1–13, https://dx.doi.org/10.1093/acrefore/9780190228620.013.593.

  62. Lohmann D, Nolte-Holube R and Raschke E 1996 A large-scale horizontal routing model to be coupled to land surface parametrization schemes; Tellus A 48(5) 708–721, https://doi.org/10.1034/j.1600-0870.1996.t01-3-00009.x.

    Article  Google Scholar 

  63. Lohmann D, Raschke E, Nijssen B and Lettenmaier D 1998 Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model; Hydrol. Sci. J. 43(1) 131–141, https://doi.org/10.1080/02626669809492107.

  64. Lorenz E 2000 The butterfly effect; World Scientific Series on Nonlinear Science Series A 39 91–94.

    Article  Google Scholar 

  65. Ma L, Zhang T, Li Q, Frauenfeld O W and Qin D 2008 Evaluation of ERA-40, NCEP-1and NCEP-2 reanalysis air temperatures with ground-based measurements in China; J. Geophys. Res. Atmos. 113 (D15), https://doi.org/10.1029/2007JD009549.

  66. Ma L, Zhang T, Frauenfeld O W, Ye B, Yang D and Qin D 2009 Evaluation of precipitation from the ERA-40, NCEP-1 and NCEP-2 Reanalyses and CMAP-1, CMAP-2 and GPCP-2 with ground-based measurements in China; J. Geophys. Res. Atmos. 114(D9), https://doi.org/10.1029/2008JD011178.

  67. Madhura R K, Krishnan R, Revadekar J V, Mujumdar M and Goswami B N 2015 Changes in western disturbances over the Western Himalayas in a warming environment; Clim. Dyn. 44(3–4) 1157–1168, https://doi.org/10.1007/s00382-014-2166-9.

    Article  Google Scholar 

  68. Mao J, Shi X, Ma L, Kaise, D P, Li Q and Thornton P E 2010 Assessment of reanalysis daily extreme temperatures with China’s homogenized historical dataset during 1979–2001 using probability density functions; J. Clim. 23(24) 6605–6623, https://doi.org/10.1175/2010JCLI3581.1.

    Article  Google Scholar 

  69. May W 2011 The sensitivity of the Indian summer monsoon to a global warming of 2°C with respect to pre-industrial times; Clim. Dyn. 37(9–10) 1843–1868, https://doi.org/10.1007/s00382-010-0942-8.

    Article  Google Scholar 

  70. Medina S, Houze R A, Kumar A and Niyogi D 2010 Summer monsoon convection in the Himalayan region: Terrain and land cover effects; Quart. J. Roy. Meteorol. Soc. 136(648) 593–616, https://doi.org/10.1002/qj.601.

    Article  Google Scholar 

  71. Meehl G and Arblaster J 2003 Mechanisms for projected future changes in south Asian monsoon precipitation; Clim. Dyn. 21(7–8) 659–675, https://doi.org/10.1007/s00382-003-0343-3.

    Article  Google Scholar 

  72. Mesquita M D S, Veldore A, Li L, Krishnan R, Orsolini Y, Senan R, Ramarao M and Viste V 2016 Forecasting India’s water future; EOS 97, https://eos.org/science-updates/forecasting-indias-water-future.

  73. Mesquita M D S, Orsolini Y J, Pal I, Veldore V, Li L, Raghavan K, Panandiker A M, Honnungar V, Gochis D, Burkhart J F 2019 Challenges in forecasting water resources of the Indus River Basin: Lessons From the analysis and modeling of atmospheric and hydrological processes; In: Indus River Basin: Water security and sustainability (eds) Khan S I and Adams T E, Elsevier, Amsterdam, pp. 57–79, https://doi.org/10.1016/B978-0-12-812782-7.00003-5.

  74. Messerli B, Viviroli D and Weingartner R 2004 Mountains of the world: vulnerable water towers for the 21st century; Ambio Special Report. 13 29–34, https://www.jstor.org/stable/25094585.

    Google Scholar 

  75. Mishra V 2015 Climatic uncertainty in Himalayan water towers. J. Geophys. Res. Atmospheres 120(7) 2689–2705. https://doi.org/10.1002/2014JD022650.

    Article  Google Scholar 

  76. Moss R H and Schneider S H 2000 Uncertainties in the IPCC TAR: Recommendations to lead authors for more consistent assessment and reporting; In: Guidance Papers on the Cross Cutting Issues of the Third Assessment Report of the IPCC (eds) Pachauri R, Taniguchi T and Tanaka K, World Meteorological Organization, Geneva, pp. 33–51,

    Google Scholar 

  77. Mujumdar M, Preethi B, Sabin T P, Ashok K, Saeed S, Pai D and Krishnan R 2012 The Asian summer monsoon response to the La Nina event of 2010; Meteorol. Appl. 19(2) 216–225, https://doi.org/10.1002/met.1301.

    Article  Google Scholar 

  78. Murphy J M, Sexton D M, Barnett D N, Jones G S, Webb M J, Collins M and Stainforth D A 2004 Quantification of modelling uncertainties in a large ensemble of climate change simulations; Nature 430(7001) 768–772, https://doi.org/10.1038/nature02771.

    Article  Google Scholar 

  79. Nash J E and Sutcliffe J V 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.

  80. Nijssen B, Lettenmaier D P, Liang X, Wetzel S W and Wood E F 1997 Streamflow simulation for continental-scale river basins; Water Resour. Res. 33(4) 711–724, https://doi.org/10.1029/96WR03517.

    Article  Google Scholar 

  81. Nijssen B, O’donnell G M, Hamlet A F and Lettenmaier D P 2001a Hydrologic sensitivity of global rivers to climate change; Clim. Change 50(1–2) 143–175, https://doi.org/10.1023/A:1010616428763.

    Article  Google Scholar 

  82. Nijssen B, O’Donnell G M, Lettenmaier D P, Lohmann D and Wood E F 2001b Predicting the discharge of global rivers; J. Clim. 14(15) 3307–3323, https://doi.org/10.1175/1520-0442(2001)014%3C3307:PTDOGR%3E2.0.CO;2.

    Article  Google Scholar 

  83. Niroula S, Halder S and Ghosh S 2018 Perturbations in the initial soil moisture conditions: Impacts on hydrologic simulation in a large river basin; J. Hydrol. 561 509–522, https://doi.org/10.1016/j.jhydrol.2018.04.029.

    Article  Google Scholar 

  84. Nohara D, Kitoh A, Hosaka M and Oki T 2006 Impact of climate change on river discharge projected by multimodel ensemble; J. Hydrometeorol. 7(5) 1076–1089, https://doi.org/10.1175/JHM531.1.

    Article  Google Scholar 

  85. Oubeidillah A A, Kao S-C, Ashfaq M, Naz B S and Tootle G 2014 A large-scale, high-resolution hydrological model parameter data set for climate change impact assessment for the conterminous US; Hydrol. Earth Syst. Sci. 18(1) 67–84, https://doi.org/10.5194/hess-18-67-2014.

    Article  Google Scholar 

  86. Palazzi E, Hardenberg J V and Provenzale A 2013 Precipitation in the Hindu‐Kush Karakoram Himalaya: Observations and future scenarios; J. Geophys. Res.: Atmospheres 118(1) 85–100, https://doi.org/10.1029/2012JD018697.

  87. Penman H L 1948 Natural evaporation from open water, bare soil and grass; Roy. Soc. London Ser. A. Math. Phys. Sci. 193(1032) 120–145, https://doi.org/10.1098/rspa.1948.0037.

  88. Pepin N, Bradley R S, Diaz H F, Baraer M, Caceres E B, Forsythe N, Fowler H, Greenwood G, Hashmi M Z, Liu X D, Miller J R, Ning L, Ohmura A, Palazzi E, Rangwala I, Schöner W, Severskiy I, Shahgedanova M, Wang M B, Williamson S N and Yang D Q 2015 Elevation-dependent warming in mountain regions of the world; Nat. Clim. Change 5(5) 424–430, https://dx.doi.org/10.1038/nclimate2563.

    Article  Google Scholar 

  89. Pomeroy J W, Toth B, Granger R J, Hedstrom N R and Essery R L H 2003 Variation in surface energetics during snowmelt in a subarctic mountain catchment; J. Hydrometeorol. 4(4) 702–719, https://doi.org/10.1175/1525-7541(2003)004<0702:VISEDS>2.0.CO;2.

    Article  Google Scholar 

  90. Priya P 2017 Modelling studies on hydro-meteorological response of Indus river basin to heavy monsoon rain events under changing climate; Doctor of Philosophy, Savitribai Phule Pune University (Th. 14187).

  91. Priya P, Krishnan R, Mujumdar M and Houze R A 2017 Changing monsoon and midlatitude circulation interactions over the Western Himalayas and possible links to occurrences of extreme precipitation; Clim. Dyn. 49(7–8) 2351–2364, https://doi.org/10.1007/s00382-016-3458-z.

    Article  Google Scholar 

  92. Priya P, Mujumdar M, Sabin T P, Terray P and Krishnan R 2015 Impacts of Indo-Pacific sea surface temperature anomalies on the summer monsoon circulation and heavy precipitation over northwest India–Pakistan region during 2010; J. Clim. 28(9) 3714–3730, https://doi.org/10.1175/JCLI-D-14-00595.1.

    Article  Google Scholar 

  93. Raje D and 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), https://doi.org/10.1029/2011WR011123.

  94. Raje D, Priya P and Krishnan R 2014 Macroscale hydrological modelling approach for study of large scale hydrologic impacts under climate change in Indian river basins; Hydrol. Process. 28(4) 1874–1889, https://doi.org/10.1002/hyp.9731.

    Article  Google Scholar 

  95. Rasmussen K L, Hill A J, Toma V E, Zuluaga M D, Webster P J and Houze Jr RA 2015 Multiscale analysis of three consecutive years of anomalous flooding in Pakistan; Quart. J. Roy. Meteorol. Soc. 141(689) 1259–1276, https://doi.org/10.1002/qj.2433.

    Article  Google Scholar 

  96. Reynolds C A, Jackson T J and Rawls W J 2000 Estimating soil water-holding capacities by linking the Food and Agriculture Organization soil map of the world with global pedon databases and continuous pedotransfer functions; Water. Resour. Res. 36(12) 3653–3662, https://doi.org/10.1029/2000WR900130

    Article  Google Scholar 

  97. Sabade S S, Kulkarni A and Kripalani R H 2011 Projected changes in South Asian summer monsoon by multi-model global warming experiments; Theor. Appl. Climatol. 103(3–4) 543–565, https://doi.org/10.1007/s00704-010-0296-5.

    Article  Google Scholar 

  98. Schumann G P, Neal J C, Voisin N, Andreadis K M, Pappenberger F, Phanthuwongpakdee N, Hall A C, Bates P D 2013 A first large‐scale flood inundation forecasting model. Water. Resour. Res. 49(10) 6248–6257, https://doi.org/10.1002/wrcr.20521.

    Article  Google Scholar 

  99. Seneviratne S I, Corti T, Davin E L, Hirschi M, Jaeger E B, Lehner I, Orlowsky B and Teuling A J 2010 Investigating soil moisture–climate interactions in a changing climate: A review; Earth Sci. Rev. 99(3–4) 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004.

    Article  Google Scholar 

  100. Sharif M and Burn D H 2007 Improved K-nearest neighbor weather generating model; J. Hydrol. Eng. 12(1) 42–51, https://doi.org/10.1061/(ASCE)1084-0699(2007)12:1(42).

    Article  Google Scholar 

  101. Shrestha K Y, Webster P J and Toma V E 2014 An atmospheric–hydrologic forecasting scheme for the Indus River basin; J. Hydrometeorol. 15(2) 861–890, https://doi.org/10.1175/JHM-D-13-051.1.

    Article  Google Scholar 

  102. Shrestha A B, Nisha Wagla and Rajbhandari R 2019 A review on the projected changes in climate over the Indus Basin; In: Indus River Basin: Water Security and Sustainability (eds) Khan S I and Adams T E, Elsevier, pp. 145–157, https://doi.org/10.1016/B978-0-12-812782-7.00007-2.

  103. Shuttleworth W J 1993 Evaporation; In: Hand book of Hydrology (ed.) Maidment D R, McGraw-Hill, New York, Chapter 4, pp. 4.1–4.53.

  104. Tennessee Valley Authority 1972 Heat and mass transfer between a water surface and the atmosphere; Tennessee Valley Authority, Norris, TN. Laboratory report no. 14, Water resources research report no. 0-6803.

  105. Thornton P E, Hasenauer H and White M A 2000 Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: an application over complex terrain in Austria; Agric. For. Meteorol. 104(4) 255–271, https://doi.org/10.1016/S0168-1923(00)00170-2.

    Article  Google Scholar 

  106. Thornton P E and Running S W 1999 An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity and precipitation; Agric. For. Meteorol. 93(4) 211–228, https://doi.org/10.1016/S0168-1923(98)00126-9.

    Article  Google Scholar 

  107. Thornton P E, Running S W and White M A 1997 Generating surfaces of daily meteorological variables over large regions of complex terrain; J. Hydrol. 190(3–4) 214–251, https://doi.org/10.1016/S0022-1694(96)03128-9.

    Article  Google Scholar 

  108. Torralba V, Doblas-Reyes F J and Gonzalez-Reviriego N 2017 Uncertainty in recent near-surface wind speed trends: A global reanalysis intercomparison; Environ. Res. Lett. 12(11) 114019, https://doi.org/10.1088/1748-9326/aa8a58.

    Article  Google Scholar 

  109. Troy T J, Wood E F and Sheffield J 2008 An efficient calibration method for continental-scale land surface modeling; Water Resour. Res. 44(9) W09411, https://doi.org/10.1029/2007WR006513.

    Article  Google Scholar 

  110. Turner A G and Annamalai H 2012 Climate change and the South Asian summer monsoon; Nat. Clim. Chang. 2(8) 587–595, https://doi.org/10.1038/nclimate1495.

    Article  Google Scholar 

  111. Viviroli D, Dürr H H, Messerli B, Meybeck M and Weingartner R 2007 Mountains of the world, water towers for humanity: Typology, mapping and global significance; Water Resour. Res. 43(7) 07447, https://doi.org/10.1029/2006WR005653.

    Article  Google Scholar 

  112. Wang A and Zeng X 2012 Evaluation of multi-reanalysis products with in situ observations over the Tibetan Plateau; J. Geophys. Res.: Atmospheres 117(D5), https://doi.org/10.1029/2011JD016553.

  113. Webster P J, Toma V E and Kim H M 2011 Were the 2010 Pakistan floods predictable? Geophys. Res. Lett. 38(4), https://doi.org/10.1029/2010GL046346.

  114. Wu H, Adler R F, Tian Y, Huffman G J, Li H and Wang J 2014 Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model; Water Resour. Res. 50(3) 2693–2717, https://doi.org/10.1002/2013WR014710.

    Article  Google Scholar 

  115. Xie A, Ren J, Qin X and Kang S 2007 Reliability of NCEP/NCAR reanalysis data in the Himalayas/Tibetan Plateau; J. Geogr. Sci. 17(4) 421–430, https://doi.org/10.1007/s11442-007-0421-2.

    Article  Google Scholar 

  116. Yin Z Y, Zhang X, Liu X, Colella M and Chen X 2008 An assessment of the biases of satellite rainfall estimates over the Tibetan Plateau and correction methods based on topographic analysis; J. Hydrometeorol. 9(3) 301–326, https://doi.org/10.1175/2007JHM903.1.

    Article  Google Scholar 

  117. Zou H, Zhu J, Zhou L, Li P and Ma S 2014 Validation and application of reanalysis temperature data over the Tibetan Plateau; J. Meteorol. Res. 28 139–149, https://doi.org/10.1007/s13351-014-3027-5.

    Article  Google Scholar 

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Acknowledgements

Authors thank IITM for extending all support for this research work. This work is carried out under the MoES-Belmont Forum Project PACMEDY. Authors acknowledge NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, for providing GPCP Precipitation data and CPC Global Unified Precipitation data from their website at https://www.esrl.noaa.gov/psd. Authors thank American Meteorological Society for providing permission to digitize the time series data from a figure from Shrestha et al. (2014). Authors also thank anonymous reviewers for their critical comments which have greatly improved manuscript.

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Supplementary materials pertaining to this article are available on the Journal of Earth Science Website (http://www.ias.ac.in/Journals/Journal_of_Earth_System_Science).

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Priya, P., Krishnan, R. & Mujumdar, M. Uncertainties in river discharge simulations of the upper Indus basin in the Western Himalayas. J Earth Syst Sci 129, 150 (2020). https://doi.org/10.1007/s12040-020-01409-w

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Keywords

  • Uncertainty analysis
  • observed precipitation and reanalysis products
  • semi-distributed macro scale VIC hydrology model
  • Upper Indus Basin
  • Western Himalaya
  • heavy summer monsoon rains and floods during 2010