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Journal of Mountain Science

, Volume 15, Issue 10, pp 2218–2235 | Cite as

Projection of future streamflow of the Hunza River Basin, Karakoram Range (Pakistan) using HBV hydrological model

  • Ayaz Fateh Ali
  • Cun-de Xiao
  • Xiao-peng Zhang
  • Muhammad Adnan
  • Mudassar Iqbal
  • Garee Khan
Article
  • 47 Downloads

Abstract

Hydrologiska Byrans Vattenbalansavdeling (HBV) Light model was used to evaluate the performance of the model in response to climate change in the snowy and glaciated catchment area of Hunza River Basin. The study aimed to understand the temporal variation of streamflow of Hunza River and its contribution to Indus River System (IRS). HBV model performed fairly well both during calibration (R2=0.87, Reff=0.85, PBIAS=−0.36) and validation (R2=0.86, Reff=0.83, PBIAS=−13.58) periods on daily time scale in the Hunza River Basin. Model performed better on monthly time scale with slightly underestimated low flows period during both calibration (R2=0.94, Reff=0.88, PBIAS=0.47) and validation (R2=0.92, Reff=0.85, PBIAS=15.83) periods. Simulated streamflow analysis from 1995–2010 unveiled that the average percentage contribution of snow, rain and glacier melt to the streamflow of Hunza River is about 16.5%, 19.4% and 64% respectively. In addition, the HBV-Light model performance was also evaluated for prediction of future streamflow in the Hunza River using future projected data of three General Circulation Model (GCMs) i.e. BCC-CSM1.1, CanESM2, and MIROCESM under RCP2.6, 4.5 and 8.5 and predictions were made over three time periods, 2010–2039, 2040–2069 and 2070–2099, using 1980–2010 as the control period. Overall projected climate results reveal that temperature and precipitation are the most sensitive parameters to the streamflow of Hunza River. MIROC-ESM predicted the highest increase in the future streamflow of the Hunza River due to increase in temperature and precipitation under RCP4.5 and 8.5 scenarios from 2010–2099 while predicted slight increase in the streamflow under RCP2.6 during the start and end of the 21th century. However, BCCCSM1.1 predicted decrease in the streamflow under RCP8.5 due to decrease in temperature and precipitation from 2010–2099. However, CanESM2 predicted 22%-88% increase in the streamflow under RCP4.5 from 2010–2099. The results of this study could be useful for decision making and effective future strategic plans for water management and their sustainability in the region.

Keywords

HBV Light model Hydrological modeling Hunza River Upper Indus Basin Snow and glacier-melt 

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Notes

Acknowledgements

This study is supported by the National Natural Science foundation of China (Grant Nos. 41690145 and 41670158). We are thankful to the Surface Water Hydrology Project (SWHP) of Water and Power Development Authority (WAPDA) and Pakistan Meteorological Department (PMD) for providing the hydro-meteorological data to conduct this study.

References

  1. Adnan M, Nabi G, Kang S, et al. (2017) Snowmelt runoff modelling under projected climate change patterns in the Gilgit River Basin of Northern Pakistan. Polish Journal of Environmental Studies 26: 525–542.  https://doi.org/10.15244/pjoes/66719 CrossRefGoogle Scholar
  2. Akhtar M, Ahmad N, Booij MJ (2008) The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios. Journal of hydrology 355: 148–163.  https://doi.org/10.1016/j.jhydrol.2008.03.015 CrossRefGoogle Scholar
  3. Ali AF, Xiao C, Anjum MN, et al. (2017) Evaluation and Comparison of TRMM Multi–Satellite Precipitation Products With Reference to Rain Gauge Observations in Hunza River Basin, Karakoram Range, Northern Pakistan. Sustainability 9(11).  https://doi.org/10.3390/su9111954 Google Scholar
  4. Archer D (2003) Contrasting hydrological regimes in the upper Indus Basin. Journal of Hydrology 274: 198–210.  https://doi.org/10.1016/S0022-1694(02)00414-6 CrossRefGoogle Scholar
  5. Arnell NW (2004) Climate change and global water resources: SRES emissions and socio–economic scenarios. Global Environmental Change 14: 31–52.  https://doi.org/10.1016/j.gloenvcha.2003.10.006 CrossRefGoogle Scholar
  6. Berg P, Feldmann H, Panitz HJ (2012) Bias correction of high resolution regional climate model data. Journal of Hydrology 448: 80–92.  https://doi.org/10.1016/j.jhydrol.2012.04.026 CrossRefGoogle Scholar
  7. Bergström S (1992) The HBV–Model–Its Structure and Applications. SMHI Reports RH No. 4, Norrköping.Google Scholar
  8. Bergstrom S (1976) Development and application of a conceptual runoff model for Scandinavian catchments. SMHI Norrköping, Report RH07.Google Scholar
  9. Bocchiola D, Diolaiuti G (2013) Recent (1980–2009) evidence of climate change in the upper Karakoram, Pakistan. Theoretical and Applied Climatology 113: 611–641.  https://doi.org/10.1007/s00704-012-0803-y CrossRefGoogle Scholar
  10. Bolch T, Kulkarni AV, Kaab A, et al. (2012) The state and fate of Himalayan glaciers. Science 336: 310–314.  https://doi.org/10.1126/science.1215828 CrossRefGoogle Scholar
  11. Braun L, Renner CB (1992) Application of a conceptual runoff model in different physiographic regions of Switzerland. Hydrological Sciences Journal 37: 217–231.  https://doi.org/10.1080/02626669209492583 CrossRefGoogle Scholar
  12. Burhan A, Waheed I, Syed AAB, et al. (2015) Generation of high–resolution gridded climate fields for the Upper Indus River Basin by Downscaling Cmip5 Outputs. Journal of Earth Science and Climatic Change 6(2).  https://doi.org/10.4172/2157-7617.1000254 Google Scholar
  13. Chiew FHS, Kirono DGC, Kent DM, et al. (2010) Comparison of runoff modelled using rainfall from different downscaling methods for historical and future climates. Journal of Hydrology 387: 10–23.  https://doi.org/10.1016/j.jhydrol.2010.03.025 CrossRefGoogle Scholar
  14. Gardelle J, Berthier E, Arnaud Y (2012) Slight mass gain of Karakoram glaciers in the early twenty–first century. Nature geoscience 5: 322–325.  https://doi.org/10.1038/ngeo1450 CrossRefGoogle Scholar
  15. Garee K, Chen X, Bao A, et al. (2017) Hydrological Modeling of the Upper Indus Basin: A Case Study from a High–Altitude Glacierized Catchment Hunza. Water 9(1).  https://doi.org/10.3390/w9010017 Google Scholar
  16. Gul C, Kang S, Ghauri B, et al. (2017) Using Landsat images to monitor changes in the snow–covered area of selected glaciers in northern Pakistan. Journal of Mountain Science 14: 1–15.  https://doi.org/10.1007/s11629-016-4097-x CrossRefGoogle Scholar
  17. Hewitt K (2005) The Karakoram anomaly? Glacier expansion and the ‘elevation effect’, Karakoram Himalaya. Mountain Research and Development 25: 332–340.  https://doi.org/10.1659/0276-4741(2005)025[0332:TKAGEA]2.0.CO;2 CrossRefGoogle Scholar
  18. Hewitt K (2011) Glacier change, concentration, and elevation effects in the Karakoram Himalaya. Upper Indus Basin. Mountain Research and Development 31: 188–200.  https://doi.org/10.1659/MRD-JOURNAL-D-11-00020.1 CrossRefGoogle Scholar
  19. Hock R (2003) Temperature index melt modelling in mountain areas. Journal of hydrology 282: 104–115.  https://doi.org/10.1016/S0022-1694(03)00257-9 CrossRefGoogle Scholar
  20. Immerzeel WW, Pellicciotti F, Bierkens MFP (2013) Rising river flows throughout the twenty–first century in two Himalayan glacierized watersheds. Nature Geoscience 6: 742–745.  https://doi.org/10.1038/NGEO1896 CrossRefGoogle Scholar
  21. Immerzeel WW, Van Beek LPH, Bierkens MFP (2010) Climate change will affect the Asian water towers. Science 328: 1382–1385.  https://doi.org/10.1126/science.1183188 CrossRefGoogle Scholar
  22. IPCC (2007) Summary for policymakers. In: Solomon S, Qin D, Manning M, et al. (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.Google Scholar
  23. IPCC (2013) Summary for Policymakers. In: Stocker TF, Qin D, Plattner GK, et al. (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.Google Scholar
  24. Iqbal M, Wen J, Wang X, et al. (2018) Assessment of air temeperature trends in the Source Region of Yellow River and its sub–basins, China. Asia–Pacific journal of Atmospheric Sciences 54(1): 111–123.  https://doi.org/10.1007/s13143-017-0064-x CrossRefGoogle Scholar
  25. Kay AL, Davies HN, Bell VA, et al. (2009) Comparison of uncertainty sources for climate change impacts: flood frequency in England. Climatic Change 92: 41–63.  https://doi.org/10.1007/s10584-008-9471-4 CrossRefGoogle Scholar
  26. Konz M (2003) HBV3–ETH9 User’s Manual. Internal Report Bavarian Academy of Sciences, Commission of Glaciology, Munich.Google Scholar
  27. Konz M, Seibert J (2010) On the value of glacier mass balances for hydrological model calibration. Journal of Hydrology 385: 238–246.  https://doi.org/10.1016/j.jhydrol.2010.02.025 CrossRefGoogle Scholar
  28. Kothawale DR, Revadekar JV, Kumar KR (2010) Recent trends in pre–monsoon daily temperature extremes over India. Journal of earth system science 119: 51–65.  https://doi.org/10.1007/s12040-010-0008-7 CrossRefGoogle Scholar
  29. Langsholt E, Lawrence D, Wong WK, et al. (2013) Effects of climate change in the Kolubara and Toplica Catchments, Serbia. Norwegian Water Resources and Energy Directorate, Oslo, Norway.Google Scholar
  30. Li H, Beldring S, Xu CY, et al. (2015) Integrating a glacier retreat model into a hydrological model–Case studies of three glacierised catchments in Norway and Himalayan region. Journal of Hydrology 527: 656–667.  https://doi.org/10.1016/j.jhydrol.2015.05.017 CrossRefGoogle Scholar
  31. Lindström G, Johansson B, Persson M, et al. (1997) Development and test of the distributed HBV–96 hydrological model. Journal of Hydrology 201: 272–288.  https://doi.org/10.1016/S0022-1694(97)00041-3 CrossRefGoogle Scholar
  32. Mashingia F, Mtalo F, Bruen M (2014) Validation of remotely sensed rainfall over major climatic regions in Northeast Tanzania. Physics and Chemistry of the Earth, Parts A/B/C 67–69: 55–63.  https://doi.org/10.1016/j.pce.2013.09.013 CrossRefGoogle Scholar
  33. Matsuo K, Heki K (2010) Time–variable ice loss in Asian high mountains from satellite gravimetry. Earth and Planetary Science Letters 290: 30–36.  https://doi.org/10.1016/j.epsl.2009.11.053 CrossRefGoogle Scholar
  34. Moriasi DN, Arnold JG, Van Liew MW, et al. (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 50: 885–900.  https://doi.org/10.13031/2013.23153 CrossRefGoogle Scholar
  35. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I–A discussion of principles. Journal of hydrology 10: 282–290.  https://doi.org/10.1016/0022-1694(70)90255-6 CrossRefGoogle Scholar
  36. Rasul G, Mahmood A, Sadiq A, et al. (2012) Vulnerability of the Indus delta to climate change in Pakistan. Pakistan Journal of Meteorology 8(16).Google Scholar
  37. Sagar S (2017) Hydrological modeling and climate change impact assessment using HBV Light Model: A case study of Karnali River Basin. XVI World Water Congress. International water resources association (IWRA), Cancun, Quintana Roo. Mexico.Google Scholar
  38. Sarikaya MA, Bishop MP, Shroder JF, et al. (2012) Space–based observations of Eastern Hindu Kush glaciers between 1976 and 2007, Afghanistan and Pakistan. Remote sensing letters 3: 77–84.  https://doi.org/10.1080/01431161.2010.536181 CrossRefGoogle Scholar
  39. Schmidt S, Nüsser M (2012) Changes of high altitude glaciers from 1969 to 2010 in the Trans–Himalayan Kang Yatze Massif, Ladakh, northwest India. Arctic, Antarctic, and Alpine Research 44: 107–121.  https://doi.org/10.1657/1938-4246-44.1.107 CrossRefGoogle Scholar
  40. Seibert J (1997) Estimation of parameter uncertainty in the HBV model. Hydrology Research 28: 247–262.  https://doi.org/10.2166/nh.1998.15 CrossRefGoogle Scholar
  41. Seibert J (1999) Regionalisation of parameters for a conceptual rainfall–runoff model. Agricultural and Forest Meteorology 98–99: 279–293.  https://doi.org/10.1016/S0168-1923(99)00105-7 CrossRefGoogle Scholar
  42. Seibert J, Vis MJP (2012) Teaching hydrological modeling with a user–friendly catchment–runoff–model software package. Hydrology and Earth System Sciences 16: 3315–3325.  https://doi.org/10.5194/hess-16-3315-201. CrossRefGoogle Scholar
  43. Sen PK (1968) Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association 63: 1379–1389.  https://doi.org/10.1080/01621459.1968.10480934 CrossRefGoogle Scholar
  44. Shrestha M, Koike T, Hirabayashi Y, et al. (2015) Integrated simulation of snow and glacier melt in water and energy balance–based, distributed hydrological modeling framework at Hunza River Basin of Pakistan Karakoram region. Journal of Geophysical Research Atmospheres 120: 4889–4919.  https://doi.org/10.1002/2014JD022666 CrossRefGoogle Scholar
  45. Singh P (2001) Snow and glacier hydrology. Water science and technology library Vol. 37, Springer Publishers, Netherland.Google Scholar
  46. Shen Y, Oki T, Utsumi N, et al. (2008) Projection of future world water resources under SRER scenarios: water withdrawal. Hydrological Sciences Journal 53: 11–33.  https://doi.org/10.1623/hysj.53.1.11 CrossRefGoogle Scholar
  47. Tahir AA, Chevallier P, Arnaud Y, et al. (2011) Snow cover dynamics and hydrological regime of the Hunza River basin, Karakoram Range, Northern Pakistan. Hydrology and Earth System Sciences 15: 2259–2274.  https://doi.org/10.5194/hess-15-2275-2011 CrossRefGoogle Scholar
  48. Teng J, Vaze J, Chiew FHS, et al. (2012) Estimating the Relative Uncertainties Sourced from GCMs and Hydrological Models in Modeling Climate Change Impact on Runoff. Journal of Hydrometeorology 13: 122–139.  https://doi.org/10.1175/JHM-D-11-058.1 CrossRefGoogle Scholar
  49. Terink W, Hurkmans RTWL, Torfs PJJF, et al. (2009) Bias correction of temperature and precipitation data for regional climate model application to the Rhine basin. Hydrology and Earth System Sciences Discussions 6: 5377–5413.  https://doi.org/10.5194/hessd-6-5377-2009 CrossRefGoogle Scholar
  50. 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: 12–29.  https://doi.org/10.1016/j.jhydrol.2012.05.052 CrossRefGoogle Scholar
  51. Thrasher B, Maurer EP, McKellar C, et al. (2012) Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrology and Earth System Sciences 16: 3309–3314.  https://doi.org/10.5194/hess-16-3309-2012 CrossRefGoogle Scholar
  52. Uhlenbrook S, Seibert J, Leibundgut C, et al. (1999) Prediction uncertainty of conceptual rainfall–runoff models caused by problems in identifying model parameters and structure. Hydrological Sciences Journal 44: 779–797.  https://doi.org/10.1080/02626669909492273 CrossRefGoogle Scholar
  53. Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: Low–flow scenarios for the River Thames, UK. Water Resources Research 420: 563–575.  https://doi.org/10.1029/2005wr004065 Google Scholar
  54. Xu CY (1999) Climate Change and Hydrologic Models: A Review of Existing Gaps and Recent Research Developments. Water Resources Management 13: 369–382.  https://doi.org/10.1023/A:1008190900459 CrossRefGoogle Scholar
  55. Xu M, Han H, Kang S (2017) Modeling Glacier Mass Balance and Runoff in the Koxkar River Basin on the South Slope of the Tianshan Mountains, China, from 1959 to 2009. Water 9(2).  https://doi.org/10.3390/w9020100 Google Scholar
  56. Zhang Y, You Q, Chen C, et al. (2016) Impacts of climate change on streamflows under RCP scenarios: A case study in Xin River Basin, China. Atmospheric Research 178–179: 521–534.  https://doi.org/10.1016/j.atmosres.2016.04.0 CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhouChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.State Key Laboratory of Land Surface and Resource EcologyBeijing Normal UniversityBeijingChina
  4. 4.Key Laboratory of land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhouChina
  5. 5.Department of Earth SciencesKarakoram International UniversityGilgitPakistan

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