Advertisement

Chinese Geographical Science

, Volume 29, Issue 1, pp 37–57 | Cite as

Evaluating Suitability of Multiple Precipitation Products for the Lancang River Basin

  • Xiongpeng Tang
  • Jianyun Zhang
  • Guoqing WangEmail author
  • Qinli Yang
  • Yanqing Yang
  • Tiesheng Guan
  • Cuishan Liu
  • Junliang Jin
  • Yanli Liu
  • Zhenxin Bao
Article
  • 8 Downloads

Abstract

Global reanalysis precipitation products could provide valuable meteorological information for flow forecasting in poorly gauged areas, helping to overcome a long-standing challenge in the field. But not all data sources are suitable for all regions or perform the same way in hydrological modeling, so it is essential to test the suitability of precipitation products before applying them. In this study, five widely used global high-resolution precipitation products—Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR), Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), China Gauge-based Daily Precipitation Analysis developed by China Meteorological Administration (CMA) and Agricultural Model Intercomparison and Improvement Project based on the NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA)—were evaluated using statistical methods and a hydrological approach for their suitability for the Lancang River Basin. The results indicated that APHRODITE, CMA, AgMERRA and CHIRPS were more accurate precipitation indicators than NCEP-CFSR in terms of the multiyear average and seasonal spatial distribution pattern, all of the CHIRPS, AgMERRA and APHRODITE perform better than CMA and NCEP-CFSR at the small, medium and high precipitation intensities ranges in subbasin11 and sunbabsin46. All five products performed better in subbasin46 (a low-altitude region) than in subbasin11 (a high-altitude region) on the daily and monthly scales. In addition to NCEP-CFSR, the other four products all presented encouraging potential for streamflow simulation at daily (Yunjinghong) and monthly (Yunjinghong, Jiuzhou and Gajiu) scale. Hydrological simulations forced with APHRODITE were the best of the five for the Yunjinghong station in capturing daily and monthly measured streamflow. Except for NCEP-CFSR, all products were very good for hydrological simulations for the Gajiu and Jiuzhou stations.

Keywords

multiple precipitation products suitability evaluation the Lancang River Basin 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abbaspour K C, Vejdani M, Haghighat S, 2007. SWAT-CUP calibration and uncertainty programs for SWAT. In: MODSIM 2007 International Congress on Modelling and Simulation: Land, Water and Environmental Management: Integrated Systems for Sustainability. Christchurch, New Zealand: Modelling and Simulation Society of Australia and New Zealand, 1603–1609.Google Scholar
  2. Alazzy A A, Lü H S, Chen R S et al., 2017. Evaluation of satellite precipitation products and their potential influence on hydrological modeling over the Ganzi river basin of the Tibetan plateau. Advances in Meteorology, 2017: 3695285. doi: 10.1155/2017/3695285CrossRefGoogle Scholar
  3. Arnold J G, Srinivasan R, Muttiah R S et al., 1998. Large area hydrologic modeling and assessment part I: model development. Journal of the American Water Resources Association, 34(1): 73–89. doi: 10.1111/j.1752-1688.1998.tb05961.xCrossRefGoogle Scholar
  4. Arnold J G, Moriasi D N, Gassman P W et al., 2012. SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55(4): 1491–1508.CrossRefGoogle Scholar
  5. Bao X H, Zhang F Q, 2013. Evaluation of NCEP–CFSR, NCEP–NCAR, ERA-Interim, and ERA-40 reanalysis datasets against independent sounding observations over the Tibetan plateau. Journal of Climate, 26(1): 206–214. doi: 10.1175/JCLI-D-1200056.1CrossRefGoogle Scholar
  6. Bitew M M, Gebremichael M, 2011. Assessment of satellite rainfall products for streamflow simulation in medium watersheds of the Ethiopian highlands. Hydrology and Earth System Sciences, 15(4): 1147–1155. doi: 10.5194/hess-151147-2011CrossRefGoogle Scholar
  7. Chen C J, Jayasekera D L, Senarath S U S, 2015. Assessing uncertainty in precipitation and hydrological modeling in the mekong. World Environmental and Water Resources Congress, pulish online.Google Scholar
  8. Chen C J, Senarath S U S, Dima-West I M et al., 2016. Evaluation and restructuring of gridded precipitation data over the Greater Mekong Subregion. International Journal of Climatology, 37(1): 180–196. doi: 10.1002/joc.4696CrossRefGoogle Scholar
  9. Chen M Y, Shi W, Xie P P et al., 2008. Assessing objective techniques for gauge-based analyses of global daily precipitation. Journal of Geophysical Research: Atmospheres, 113(D4): D04110. doi: 10.1029/2007JD009132CrossRefGoogle Scholar
  10. de Condappa D, Chaponnière A, Lemoalle J, 2009. A decisionsupport tool for water allocation in the Volta Basin. Water International, 34(1): 71–87. doi: 10.1080/02508060802677861CrossRefGoogle Scholar
  11. Dyson M, Bergkamp G, Scanlon J, 2003. Flow: The Essentials of Environmental Flows. Gland: IUCN.CrossRefGoogle Scholar
  12. Funk C C, Peterson P J, Landsfeld M F et al., 2014. A Quasi-Global Precipitation Time Series for Drought Monitoring. Reston, VA: US Geological Survey. doi: 10.3133/ds832CrossRefGoogle Scholar
  13. Gao C, He Z G, Pan S L et al., 2018. Effects of climate change on peak runoff and flood levels in Qu River Basin, East China. Journal of Hydro-environment Research, online. doi: 10.1016/j.jher.2018.02.005.Google Scholar
  14. Gebremichael M, Bitew M M, Hirpa F A et al., 2015. Accuracy of satellite rainfall estimates in the Blue Nile Basin: lowland plain versus highland mountain. Water Resources Research, 50(11): 8775–8790. doi: 10.1002/2013WR014500CrossRefGoogle Scholar
  15. Hu B, Cui B S, Dong S K et al., 2009. Ecological water requirement (EWR) analysis of high mountain and steep gorge (HMSG) river—application to upper lancang–mekong river. Water Resources Management, 23(2): 341–366. doi: 10.1007/s11269-008-9278-0CrossRefGoogle Scholar
  16. Huang C, Li Y F, Liu G H et al., 2014. Recent climate variability and its impact on precipitation, temperature, and vegetation dynamics in the Lancang River headwater area of China. International Journal of Remote Sensing, 35(8): 2822–2834. doi: 10.1080/01431161.2014.890303CrossRefGoogle Scholar
  17. Jacobs J W, 2002. The mekong river commission: transboundary water resources planning and regional security. The Geographical Journal, 168(4): 354–364. doi: 10.1111/j.0016-7398.2002.00061.xCrossRefGoogle Scholar
  18. Jayakrishnan R, Srinivasan R, Santhi C et al., 2005. Advances in the application of the SWAT model for water resources management. Hydrological Processes, 19(3): 749–762. doi: 10.1002/hyp.5624CrossRefGoogle Scholar
  19. Jiang S H, Ren L L, Hong Y et al., 2012. Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method. Journal of Hydrology, 452–453: 213–225. doi: 10.1016/j.jhydrol.2012.05.055CrossRefGoogle Scholar
  20. Katsanos D, Retalis A, Michaelides S, 2015. Validation of a high-resolution precipitation database (CHIRPS) over Cyprus for a 30-year period. Atmospheric Research, 169: 459–464. doi: 10.1016/j.atmosres.2015.05.015CrossRefGoogle Scholar
  21. Koutsouris A J, Chen D L, Lyon S W, 2016. Comparing global precipitation data sets in eastern Africa: a case study of Kilombero Valley, Tanzania. International Journal of Climatology, 36(4): 2000–2014. doi: 10.1002/joc.4476CrossRefGoogle Scholar
  22. Lauri H, Räsänen T A, Kummu M, 2014. Using reanalysis and remotely sensed temperature and precipitation data for hydrological modeling in monsoon climate: mekong river case study. Journal of Hydrometeorology, 15(4): 1532–1545. doi: 10.1175/JHM-D-13-084.1CrossRefGoogle Scholar
  23. Li F P, Zhang YQ, Xu Z X et al., 2013. The impact of climate change on runoff in the southeastern Tibetan Plateau. Journal of Hydrology, 505: 188–201. doi: 10.1016/j.jhydrol.2013.09.052CrossRefGoogle Scholar
  24. Li L, Hong Y, Wang J H et al., 2009. Evaluation of the real-time TRMM-based multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria, Africa. Natural Hazards, 50(1): 109–123. doi: 10.1007/s11069-008-9324-5CrossRefGoogle Scholar
  25. Liu S L, Cui B S, Dong S K et al., 2008. Evaluating the influence of road networks on landscape and regional ecological risk—A case study in Lancang River Valley of Southwest China. Ecological Engineering, 34(2): 91–99. doi: 10.1016/j.ecoleng.2008.07.006CrossRefGoogle Scholar
  26. Liu S L, Wang C, Liu Q et al., 2013. Streamflow and soil erosion simulation using SWAT model in Lower-Middle Reaches of Lancang River. In: Proceedings of 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering. Atlantis Press, doi: 10.2991/rsete.2013.37Google Scholar
  27. Nachtergaele F O, van Velthuizen H, Verelst L et al., 2012. Harmonized World Soil Database (Version 1.2). Food and Agriculture Organization of the UN, International Institute for Applied Systems Analysis, ISRIC - World Soil Information, Institute of Soil Science - Chinese Academy of Sciences, Joint Research Centre of the EC.Google Scholar
  28. Nash J E, Sutcliffe J V, 1970. River flow forecasting through conceptual models part I: A discussion of principles. Journal of Hydrology, 10(3): 282–290. doi: 10.1016/0022-1694(70)90255-6CrossRefGoogle Scholar
  29. Ouyang F, Zhu Y H, Fu G B et al., 2015. Impacts of climate change under CMIP5 RCP scenarios on streamflow in the Huangnizhuang catchment. Stochastic Envirosnmental Research and Risk Assessment, 29(7): 1781–1795. doi: 10.1007/s00477-014-1018-9CrossRefGoogle Scholar
  30. Poméon T, Jackisch D, Diekkrüger B, 2017. Evaluating the performance of remotely sensed and reanalysed precipitation data over west africa using hbv light. Journal of Hydrology, 547: 222–235. doi: 10.1016/j.jhydrol.2017.01.055CrossRefGoogle Scholar
  31. Reichle R H, Koster R D, de Lannoy G J M, et al., 2011. Assessment and enhancement of MERRA land surface hydrology estimates. Journal of Climate, 24(24): 6322–6338. doi: 10.1175/JCLI-D-10-05033.1CrossRefGoogle Scholar
  32. Retalis A, Tymvios F, Katsanos D et al., 2017. Downscaling CHIRPS precipitation data: an artificial neural network modelling approach. International Journal of Remote Sensing, 38(13): 3943–3959. doi: 10.1080/01431161.2017.1312031CrossRefGoogle Scholar
  33. Rienecker M M, Suarez M J, Todling R et al., 2008. The GEOS-5 Data Assimilation System-Documentation of Versions 5.0.1, 5.1.0, and 5.2.0. Greenbelt, MD, United States: NASA Goddard Space Flight Center.Google Scholar
  34. Rienecker M M, Suarez M J, Gelaro R, et al., 2011. MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24(14): 3624–3648. doi: 10.1175/JCLI-D-11-00015.1CrossRefGoogle Scholar
  35. Saha S, Moorthi S, Pan H L et al., 2010. The NCEP climate forecast system reanalysis. Bulletin of the American Meteorological Society, 91(8): 1015–1057. doi: 10.1175/2010BAMS3001.1CrossRefGoogle Scholar
  36. Seyyedi H, Anagnostou E N, Beighley E et al., 2015. Hydrologic evaluation of satellite and reanalysis precipitation datasets over a mid-latitude basin. Atmospheric Research, 164–165: 37–48. doi: 10.1016/j.atmosres.2015.03.019Google Scholar
  37. Shrestha B, Cochrane T A, Caruso B S et al., 2016. Uncertainty in flow and sediment projections due to future climate scenarios for the 3S Rivers in the Mekong Basin. Journal of Hydrology, 540: 1088–1104. doi: 10.1016/j.jhydrol.2016.07.019CrossRefGoogle Scholar
  38. Thiemig V, Rojas R, Zambrano-Bigiarini M et al., 2013. Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. Journal of Hydrology, 499: 324–338. doi: 10.1016/j.jhydrol.2013.07.012CrossRefGoogle Scholar
  39. Thompson J R, Green A J, Kingston D G et al., 2013. Assessment of uncertainty in river flow projections for the Mekong River using multiple GCMs and hydrological models. Journal of Hydrology, 486: 1–30. doi: 10.1016/j.jhydrol.2013.01.029CrossRefGoogle Scholar
  40. Tong K, Su F G, Yang D Q et al., 2014. Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau. Journal of Hydrology, 519: 423–437. doi: 10.1016/j.jhydrol.2014.07.044CrossRefGoogle Scholar
  41. Ushio T, Kachi M, 2010. Kalman filtering applications for global satellite mapping of precipitation (GSMaP). In: Gebremichael M, Hossain F (eds). Satellite Rainfall Applications for Surface Hydrology. Dordrecht: Springer.Google Scholar
  42. Wang G Q, Zhang J Y, Jin J L et al., 2012. Assessing water resources in China using PRECIS projections and a VIC model. Hydrology and Earth System Sciences, 16(1): 231–240. doi: 10.5194/hess-16-231-2012CrossRefGoogle Scholar
  43. Wang W, Lu H, 2015. Evaluation and hydrological applications of TRMM rainfall products over the Mekong River basin with a distributied model. In: Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milan, Italy: IEEE. doi: 10.1109/IGARSS.2015.7326321Google Scholar
  44. Worqlul A W, Maathuis B, Adem A A, et al., 2014. Comparison of rainfall estimations by TRMM 3B42, MPEG and CFSR with ground-observed data for the Lake Tana basin in Ethiopia. Hydrology and Earth System Sciences, 18(18): 4871–4881. doi: 10.5194/hess-18-4871-2014CrossRefGoogle Scholar
  45. Xie P P, Chen M Y, Yang S et al., 2007. A gauge-based analysis of daily precipitation over East Asia. Journal of Hydrometeorology, 8(3): 607–626. doi: 10.1175/JHM583.1CrossRefGoogle Scholar
  46. Xue X W, Hong Y, Limaye A S et al., 2013. Statistical and hydrological evaluation of TRMM-based multi-satellite precipitation analysis over the wangchu basin of bhutan: are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? Journal of Hydrology, 499: 91–99. doi: 10.1016/j.jhydrol.2013.06.042CrossRefGoogle Scholar
  47. Yatagai A, Arakawa O, Kamiguchi K et al., 2009. A 44-year daily gridded precipitation dataset for asia based on a dense network of rain gauges. SOLA, 5(2009): 137–140. doi: 10.2151/sola.2009-035CrossRefGoogle Scholar
  48. Yatagai A, Krishnamurti T N, Kumar V et al., 2014. Use of APHRODITE rain gauge–based precipitation and TRMM 3B43 products for improving asian monsoon seasonal precipitation forecasts by the superensemble method. Journal of Climate, 27(3): 1062–1069. doi: 10.1175/JCLI-D-13-00332.1CrossRefGoogle Scholar
  49. Yatagai A, Kamiguchi K, Arakawa O et al., 2012. APHRODITE: constructing a long-term daily gridded precipitation dataset for asia based on a dense network of rain gauges. Bulletin of the American Meteorological Society, 93(9): 1401–1415. doi: 10.1175/BAMS-D-11-00122.1CrossRefGoogle Scholar
  50. Yong B, Ren L L, Hong Y et al., 2010. Hydrologic evaluation of multisatellite precipitation analysis standard precipitation products in basins beyond its inclined latitude band: a case study in Laohahe basin, China. Water Resources Research, 46(7): 759–768. doi: 10.1029/2009WR008965CrossRefGoogle Scholar
  51. Yong B, Chen B, Gourley J J et al., 2014. Intercomparison of the Version-6 and Version-7 TMPA precipitation products over high and low latitudes basins with independent gauge networks: Is the newer version better in both real-time and post-real-time analysis for water resources and hydrologic extremes? Journal of Hydrology, 508: 77–87. doi: 10.1016/j.jhydrol. 2013.10.050CrossRefGoogle Scholar
  52. Zhao Q H, Liu S L, Deng L et al., 2012. The effects of dam construction and precipitation variability on hydrologic alteration in the Lancang River Basin of southwest China. Stochastic Environmental Research and Risk Assessment, 26(7): 993–1011. doi: 10.1007/s00477-012-0583-zCrossRefGoogle Scholar
  53. Zhu Q, Xuan W D, Liu L et al., 2016. Evaluation and hydrological application of precipitation estimates derived from PERSIANNCDR, TRMM 3B42V7, and NCEPCFSR over humid regions in China. Hydrological Processes, 30(17): 3061–3083. doi: 10.1002/hyp.10846CrossRefGoogle Scholar
  54. Zhu X F, Zhang M J, Wang S J et al., 2015. Comparison of monthly precipitation derived from high-resolution gridded datasets in arid Xinjiang, central Asia. Quaternary International, 358: 160–170. doi: 10.1016/j.quaint.2014.12.027CrossRefGoogle Scholar

Copyright information

© Science Press and Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xiongpeng Tang
    • 1
    • 2
    • 3
  • Jianyun Zhang
    • 1
    • 2
  • Guoqing Wang
    • 1
    • 2
    Email author
  • Qinli Yang
    • 4
  • Yanqing Yang
    • 1
    • 2
  • Tiesheng Guan
    • 1
    • 2
  • Cuishan Liu
    • 1
    • 2
  • Junliang Jin
    • 1
    • 2
  • Yanli Liu
    • 1
    • 2
  • Zhenxin Bao
    • 1
    • 2
  1. 1.Nanjing Hydraulic Research InstituteNanjingChina
  2. 2.State Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringNanjingChina
  3. 3.Hohai UniversityNanjingChina
  4. 4.University of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations