Climate Dynamics

, Volume 53, Issue 12, pp 7447–7460 | Cite as

Seasonal drought ensemble predictions based on multiple climate models in the upper Han River Basin, China

  • Feng Ma
  • Aizhong YeEmail author
  • Qingyun Duan


An experimental seasonal drought forecasting system is developed based on 29-year (1982–2010) seasonal meteorological hindcasts generated by the climate models from the North American Multi-Model Ensemble (NMME) project. This system made use of a bias correction and spatial downscaling method, and a distributed time-variant gain model (DTVGM) hydrologic model. DTVGM was calibrated using observed daily hydrological data and its streamflow simulations achieved Nash–Sutcliffe efficiency values of 0.727 and 0.724 during calibration (1978–1995) and validation (1996–2005) periods, respectively, at the Danjiangkou reservoir station. The experimental seasonal drought forecasting system (known as NMME-DTVGM) is used to generate seasonal drought forecasts. The forecasts were evaluated against the reference forecasts (i.e., persistence forecast and climatological forecast). The NMME-DTVGM drought forecasts have higher detectability and accuracy and lower false alarm rate than the reference forecasts at different lead times (from 1 to 4 months) during the cold-dry season. No apparent advantage is shown in drought predictions during spring and summer seasons because of a long memory of the initial conditions in spring and a lower predictive skill for precipitation in summer. Overall, the NMME-based seasonal drought forecasting system has meaningful skill in predicting drought several months in advance, which can provide critical information for drought preparedness and response planning as well as the sustainable practice of water resource conservation over the basin.


Seasonal drought forecasting system NMME DTVGM Soil moisture Han River basin 



This study was supported by the Natural Science Foundation of China (No. 41475093), the Intergovernmental Key International S&T Innovation Cooperation Program (No. 2016YFE0102400) and the State Key Laboratory of Severe Weather Open Research Program (No. 2015LASW-A05).


  1. Ahmed KF (2011) Bias Correction and Downscaling of climate model outputs required for impact assessments of climate change in the US northeast. Master’s Theses pp212.
  2. Arnold JG, Williams JR, Srinivasan R (1997) Model theory of SWAT. USDA. Agricultural Research Service Grassland. Soil and Water Research Laboratory, USAGoogle Scholar
  3. Becker E, van den Dool H, Zhang Q (2014) Predictability and forecast skill in NMME. J Climate 27:5891–5906. doi: 10.1175/JCLI-D-13-00597.1 CrossRefGoogle Scholar
  4. Caffrey P, Farmer A (2014) A review of Downscaling methods for climate change projections. Tetra Tech ARDGoogle Scholar
  5. Day GN (1985) Extended streamflow forecasting using NWSRFS. J Water Resour Plann Manage Div Am Soc Civ Eng 111:157–170. doi: 10.1061/(ASCE)0733-9496(1985)111:2(157)CrossRefGoogle Scholar
  6. DelSole T, Nattala J, Tippett MK (2014) Skill improvement from increased ensemble size and model diversity. Geophys Res Lett 41:7331–7342. doi: 10.1002/2014GL060133 CrossRefGoogle Scholar
  7. Garen DC (1992) Improved techniques in regression-based streamflow volume forecasting. J Water Resour Plann Manage 118:654–670. doi: 10.1061/(ASCE)0733-9496(1992)CrossRefGoogle Scholar
  8. Huggins LF, Monke EJ (1966) The mathematical simulation of the hydrology of small watersheds. Technical Report No. 1. Purdue University Water Resource Research Center, West LafayetteGoogle Scholar
  9. Jin R, Guo H (1993) Water resources assessment in the water source areas of the Middle Route of the South to North Water Transfer Project and water quantity analysis in the Danjiangkou Reservoir. Yangzte River 24:7–12Google Scholar
  10. Kirtman BP, Min D, Infanti JM, Kinter JL, Paulino DA, Zhang Q, van den Dool H, Saha S, Pena Mendez M, Becker E, Peng P, Tripp P, Huang J, DeWitt DG, Tippett MK, Barnston AG, Li S, Rosati A, Schubert SD, Rienecker M, Suarez M, Li ZE, Marshak J, Lim Y.-K, Tribbia J, Pegion K, Merryfield WJ, Denis B, Wood EF (2014) The North American Multi-Model Ensemble (NMME): Phase-1 seasonal to interannual prediction, phase-2 toward developing intra-seasonal prediction. Bull Am Meteorol Soc 95:585–601. doi: 10.1175/BAMS-D-12-00050.1 CrossRefGoogle Scholar
  11. Li S, Zhang Q (2012) Basin ecosystem management in the Upper Han River for the South to North Water Division Project, China. J Environ Anal Toxicol S3:002. doi: 10.4172/2161-0525.S3-002 CrossRefGoogle Scholar
  12. Li S, Gu S, Liu W, Han H, Zhang Q (2008) Water quality in relation to land use and land cover in the upper Han River Basin, China. Catena 75:216–222CrossRefGoogle Scholar
  13. Li S, Liu W, Gu S, Cheng X, Xu Z, Zhang Q (2009) Spatio-temporal dynamics of nutrients in the upper Han River basin, China. J Hazard Mat 162(2–3):1340–1346. doi: 10.1016/j.jhazmat.2008.06.059 CrossRefGoogle Scholar
  14. Luo L, Wood EF (2007) Monitoring and predicting the 2007 US drought. J Geophys Res 34:L22702. doi: 10.1029/2007GL031673 CrossRefGoogle Scholar
  15. Luo L, Wood EF (2008) Use of Bayesian merging techniques in a multimodel seasonal hydrologic ensemble prediction system for the eastern United States. J Hydrometeor 9:866–884. doi: 10.1175/2008JHM980.1 CrossRefGoogle Scholar
  16. Luo L, Wood EF, Pan M (2007) Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions. J Geophys Res 112:D10102. doi: 10.1029/2006JD007655 CrossRefGoogle Scholar
  17. Ma F, Yuan X, Ye A (2015) Seasonal drought predictability and forecast skill over China. J Geophys Res 120(16):8264–8275. doi: 10.1002/2015JD023185 CrossRefGoogle Scholar
  18. Ma F, Ye A, Deng X, Zhou Z, Liu X, Duan Q, Xu J, Miao C, Di Z, Gong W (2016) Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China. Int J Climatol 26:132–144. doi: 10.1002/joc.4333 CrossRefGoogle Scholar
  19. Meng C, Yin SY (2012) Study on precipitation change and drought prediction in upper reaches of Hanjiang River during the last 50 years. Res Agri Modern 33(1):125–128Google Scholar
  20. Mo K, Lettenmaier DP (2014) Hydrologic prediction over the conterminous United States using the National Multi-Model Ensemble. J Hydrometeor 15:1457–1472. doi: 10.1175/JHM-D-13-0197.1 CrossRefGoogle Scholar
  21. Pagano T, Garen D, Sorooshian S (2004) Evaluation of official western U.S. seasonal water supply outlooks, 1922–2002. J Hydrometeor 5:896–909. doi: 10.1175/1525-7541(2004)005<0896:EOOWUS>2.0.CO;2 CrossRefGoogle Scholar
  22. Pereira L, Pereira A, Allen R, Alves I (1999) Evapotranspiration: concepts and future trend. J Irrig Drain Eng 4:45–51. doi: 10.1061/(ASCE)0733-9437(1999)CrossRefGoogle Scholar
  23. Ren L, Yin S, Peng W (2013) Statistics and causes of historical drought disasters in upper reaches of Hanjiang River. Bull Soil Water Conserv 33(1):129–371Google Scholar
  24. Sheffield J, Goteti G, Wen F, Wood EF (2004) A simulated soil moisture based drought analysis for the United States. J Geophys Res 109:D24108. doi: 10.1029/2004JD005182 CrossRefGoogle Scholar
  25. Sheffield J, Wood EF, Chaney N, Guan K, Sadri S, Yuan X, Olang L, Amani A, Ali A, Demuth S, Ogallo L (2014) A drought monitoring and forecasting system for sub-Sahara African water resources and food security. Bull Amer Meteor Soc 95:861–882. doi: 10.1175/BAMS-D-12-00124.1 CrossRefGoogle Scholar
  26. Shen D, Liu C (1998) Effects of different scales of MR-SNWTP on the down stream of the Danjiang Kou reservoir. Acta Geographica Sinica 53:341–348Google Scholar
  27. Shukla S, Lettenmaier DP (2011) Seasonal hydrologic prediction in the United States: Understanding the role of initial hydrologic conditions and seasonal climate forecast skill. Hydrol Earth Syst Sci 15:3529–3538. doi: 10.5194/hess-15-3529-2011 CrossRefGoogle Scholar
  28. Shukla S, Sheffield J, Wood EF, Lettenmaier DP (2013) On the sources of global land surface hydrologic predictability. Hydrol Earth Syst Sci 17:2781–2796. doi: 10.5194/hess-17-2781-2013 CrossRefGoogle Scholar
  29. Su B, Kundzewicz ZW, Jiang T (2008) Simulation of extreme precipitation over the Yangtze River Basin using Wakeby distribution. Theor Appl Climatol 96:209–219. doi: 10.1007/s00704-008-0025-5 CrossRefGoogle Scholar
  30. Tao X, Chen H, Xu C (2015) Characteristics of drought variations in Hanjiang Basin in 1961–2014 based on SPI/SPEI. Journal of Water Resources Research 4(5):404–415. doi: 10.12677/JWRR.2015.45050 CrossRefGoogle Scholar
  31. Van den Hurk B, Bouwer LM, Buontempo C, Döscher R, Ercin E, Hananel C, Hunink JE, Kjellström E, Klein B, Manez M, Pappenberger F, Pouget L, Ramos M-H, Ward PJ, Weerts AH, Wijngaard JB (2016) Improving predictions and management of hydrological extremes through climate services. Climate Services 1: 6–11. doi: 10.1016/j.cliser.2016.01.001 CrossRefGoogle Scholar
  32. Wang A, Lettenmaier DP, Sheffield J (2011) Soil moisture drought in China, 1950–2006. J Climate 24:3257–3271. doi: 10.1175/2011JCLI3733.1 CrossRefGoogle Scholar
  33. Wilks DS (2011) Statistical methods in the atmospheric sciences. 3rd edn. Academic Press, Salt Lake CityGoogle Scholar
  34. Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic forecasting for the eastern United States. J Geophys Res 107(D20):6–15. doi: 10.1029/2001JD000659(ACL 6–1–ACL) CrossRefGoogle Scholar
  35. Wood AW, Lettenmaier DP (2006) A test bed for new seasonal hydrologic forecasting approaches in the western United States. Bull Am Meteor Soc 87:1699–1712. doi: 10.1175/BAMS-87-12-1699 CrossRefGoogle Scholar
  36. Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62(1–3):189–216. doi: 10.1023/B:CLIM.0000013685.99609.9e CrossRefGoogle Scholar
  37. Wood EF, et al. (2011), Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water. Water Resour Res 47:W05301, doi: 10.1029/2010WR010090 CrossRefGoogle Scholar
  38. Xia J, Wang G, Lv A (2003) A research on distributed time variant gain modeling. Acta Geographica Sinica 58(5):789–796. doi: 10.11821/xb200305019 CrossRefGoogle Scholar
  39. Xia J, Wang G, Tan G, Huang GH (2005) Development of distributed time-variant gain model for nonlinear hydrological systems. Sci China Series D: Earth Sci 48(6):713–723. doi: 10.1360/03yd0183 CrossRefGoogle Scholar
  40. Xiao M, Zhang Q, Singh VP, Chen X (2016) Probabilistic forecasting of seasonal drought behaviors in the Huai River basin, China. Theore Appl Climatol. doi: 10.1007/s00704-016-1733-x CrossRefGoogle Scholar
  41. Xu Y (1998) An analysis of climatic cause for dry and rainless Han River basin during 1997. Hubei Meteorological (2):12–14 (in Chinese)Google Scholar
  42. Yang Y, Zhou N, Guo X, Hu Q (1997) The hydrology characteristics analysis of HanJiang up-streams. Hydrology 2:54–56Google Scholar
  43. Ye A, Xia J, Wang GS, Wang XN (2005) Drainage network extraction and subcatchment delineation based on digital elevation model. J Hydraul Eng 36(5):531–537 (Chinese)Google Scholar
  44. Ye A, Duan Q, Zeng H, Li L, Wang C (2010) A distributed time-variant gain hydrological model based on remote sensing. J Resour Ecol 1(3):222–230. doi: 10.3969/j.issn.1674-764x.2010.03.005 CrossRefGoogle Scholar
  45. Ye A, Duan Q, Xu J (2014) A review of hydrological ensemble forecast and case study. China Water Power Press, Beijing (Chinese) Google Scholar
  46. Yuan X (2016) An experimental seasonal hydrological forecasting system over the Yellow River basin-Part II: The added value from climate forecast models. Hydrol Earth Syst Sci 20:2453–2466. doi: 10.5194/hess-20-2453-2016 CrossRefGoogle Scholar
  47. Yuan X, Wood EF (2012) On the clustering of climate models in ensemble seasonal forecasting. Geophys Res Lett 39:L18701. doi: 10.1029/2012GL052735 CrossRefGoogle Scholar
  48. Yuan X, Wood EF, Roundy JK, Pan M (2013) CFSv2-based seasonal hydroclimatic forecasts over conterminous United States. J Climate 26:4828–4847. doi: 10.1175/JCLI-D-12-00683.1 CrossRefGoogle Scholar
  49. Yuan X, Roundy JK, Wood EF, Sheffield J (2015a) Seasonal forecasting of global hydrologic extremes: system development and evaluation over GEWEX Basins. Bull Am Meteor Soc 96:1895–1912. doi: 10.1175/BAMS-D-14-00003.1 CrossRefGoogle Scholar
  50. Yuan X, Wood EF, Ma Z (2015b) A review on climate-model-based seasonal hydrologic forecasting: physical understanding and system development. Wiley Interdisciplinary Reviews: Water 2(5): 523–536.doi: 10.1002/wat2.1088 CrossRefGoogle Scholar
  51. Yuan X, Ma F, Wang L, Zheng Z, Ma Z, Ye A, Peng S (2016) An experimental seasonal hydrological forecasting system over the Yellow River basin-Part I: Understanding the role of initial hydrological conditions. Hydrol Earth Syst Sci 20:2437–2451. doi: 10.5194/hess-20-2437-2016 CrossRefGoogle Scholar
  52. Zhai J, Su B, Krysanova V, Vetter T, Gao C, Jiang T (2010) Spatial variation and trends in PDSI and SPI indices and their relation to streamflow in 10 large regions of China. J Climate 23:649–663. doi: 10.1175/2009JCLI2968.1 CrossRefGoogle Scholar
  53. Zhang Q (2005) The South-to-North Water Diversion (SNWD) Project. Front Ecol Environ 3(2):75–76. doi: 10.2307/3868512 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  2. 2.Institute of Land Surface System and Sustainable Development, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina

Personalised recommendations