Chinese Geographical Science

, Volume 29, Issue 1, pp 13–25 | Cite as

Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin

  • Xian Luo
  • Wenqi Wu
  • Daming HeEmail author
  • Yungang Li
  • Xuan Ji


Satellite-based products with high spatial and temporal resolution provide useful precipitation information for data-sparse or ungauged large-scale watersheds. In the Lower Lancang-Mekong River Basin, rainfall stations are sparse and unevenly distributed, and the transboundary characteristic makes the collection of precipitation data more difficult, which has restricted hydrological processes simulation. In this study, daily precipitation data from four datasets (gauge observations, inverse distance weighted (IDW) data, Tropical Rainfall Measuring Mission (TRMM) estimates, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates), were applied to drive the Soil and Water Assessment Tool (SWAT) model, and then their capability for hydrological simulation in the Lower Lancang-Mekong River Basin were examined. TRMM and CHIRPS data showed good performances on precipitation estimation in the Lower Lancang-Mekong River Basin, with the better performance for TRMM product. The Nash-Sutcliffe efficiency (NSE) values of gauge, IDW, TRMM, and CHIRPS simulations during the calibration period were 0.87, 0.86, 0.95, and 0.93 for monthly flow, respectively, and those for daily flow were 0.75, 0.77, 0.86, and 0.84, respectively. TRMM and CHIRPS data were superior to rain gauge and IDW data for driving the hydrological model, and TRMM data produced the best simulation performance. Satellite-based precipitation estimates could be suitable data sources when simulating hydrological processes for large data-poor or ungauged watersheds, especially in international river basins for which precipitation observations are difficult to collect. CHIRPS data provide long precipitation time series from 1981 to near present and thus could be used as an alternative precipitation input for hydrological simulation, especially for the period without TRMM data. For satellite-based precipitation products, the differences in the occurrence frequencies and amounts of precipitation with different intensities would affect simulation results of water balance components, which should be comprehensively considered in water resources estimation and planning.


hydrological simulation satellite-based precipitation estimates spatial distribution of precipitation international river the Lower Lancang-Mekong River Basin 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abbaspour K C, Rouholahnejad E, Vaghefi S et al., 2015. A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution largescale SWAT model. Journal of Hydrology, 524: 733–752. doi: 10.1016/j.jhydrol.2015.03.027CrossRefGoogle Scholar
  2. AghaKouchak A, Nakhjiri N, 2012. A near real-time satellite-based global drought climate data record. Environmental Research Letters, 7: 044037. doi: 10.1088/1748-9326/7/4/044037CrossRefGoogle Scholar
  3. Arnold J G, Srinivasan R, Muttiah R S et al., 1998. Large area hydrologic modeling and assessment part ?: model development. Journal of the American Water Resources Association, 34(1): 73–89.CrossRefGoogle Scholar
  4. Arnold J G, Youssef M A, Yen H et al., 2015. Hydrological processes and model representation: impact of soft data on calibration. Transactions of the American Society of Agricultural and Biological Engineers, 58(6): 1637–1660. doi: 10.13031/trans.58.10726Google Scholar
  5. Cho J, Bosch D, Lowrance R et al., 2009. Effect of spatial distribution of rainfall on temporal and spatial uncertainty of SWAT output. Transactions of the American Society of Agricultural and Biological Engineers, 52(5): 1545–1555. doi: 10.13031/2013.29143Google Scholar
  6. Duan Z, Liu J, Tuo Y et al., 2016. Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. Science of the Total Environment, 573: 1536–1553. doi: 10.1016/j.scitotenv.2016. 08.213CrossRefGoogle Scholar
  7. Fan H, He D, Wang H, 2015. Environmental consequences of damming the mainstream Lancang-Mekong River: a review. Earth-Science Reviews, 146: 77–91. doi: 10.1016/j.earscirev.2015.03.007CrossRefGoogle Scholar
  8. Galván L, Olías M, Izquierdo T et al., 2014. Rainfall estimation in SWAT: an alternative method to simulate orographic precipitation. Journal of Hydrology, 509: 257–265. doi: 10.1016/j.jhydrol.2013.11.044CrossRefGoogle Scholar
  9. Gao J, Sheshukov A Y, Yen H et al., 2017. Impacts of alternative climate information on hydrologic processes with SWAT: a comparison of NCDC, PRISM and NEXRAD datasets. Catena, 156: 353–364. doi: 10.1016/j.catena.2017.04.010CrossRefGoogle Scholar
  10. Haberlandt U, 2007. Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event. Journal of Hydrology, 332: 144–157. doi: 10.1016/j.jhydrol.2006.06.028CrossRefGoogle Scholar
  11. Jiang Shanhu, Ren Liliang, Yong Bin et al., 2016. Evaluation of latest TMPA and CMORPH precipitation products with independent rain gauge observation networks over high-latitude and low-latitude basins in China. Chinese Geographical Science, 26(4): 439–455. doi: 10.1007/s11769-016-0818-xCrossRefGoogle Scholar
  12. Jin Xin, He Chansheng, Zhang Lanhui et al., 2018. A modified groundwater module in SWAT for improved streamflow simulation in a large, arid endorheic river watershed in Northwest China. Chinese Geographical Science, 28(1): 47–60. doi: 10.1007/s11769-018-0931-0CrossRefGoogle Scholar
  13. Johnston R, Kummu M, 2012. Water resource models in the Mekong Basin: a review. Water Resources Management, 26(2): 429–455. doi: 10.1007/s11269-011-9925-8CrossRefGoogle Scholar
  14. Katiraie-Boroujerdy P S, Asanjan A A, Hsu K et al., 2017. Intercomparison of PERSIANN-CDR and TRMM-3B42V7 precipitation estimates at monthly and daily time scales. Atmospheric Research, 193: 36–49. doi: 10.1016/j.atmosres.2017.04.005CrossRefGoogle Scholar
  15. Katsanos D, Retalis A, Michaelides S, 2016. 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
  16. Li D, Long D, Zhao J et al., 2017. Observed changes in flow regimes in the Mekong River basin. Journal of Hydrology, 551: 217–232. doi: 10.1016/j.jhydrol.2017.05.061CrossRefGoogle Scholar
  17. Li X, Zhang Q, Xu C, 2012. Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment, Poyang lake basin. Journal of Hydrology, 426–427: 28–38. doi: 10.1016/j.jhydrol.2012.01.013CrossRefGoogle Scholar
  18. Liu Miao, Li Chunlin, Hu Yuanman et al., 2014. Combining CLUE-S and SWAT models to forecast land use change and non-point source pollution impact at a watershed scale in Liaoning province, China. Chinese Geographical Science, 24(5): 540–550. doi: 10.1007/s11769-014-0661-xCrossRefGoogle Scholar
  19. Ly S, Charles C, Degré A, 2011. Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrology and Earth System Sciences, 15: 2259–2274. doi: 10.5194/hess-15-2259-2011CrossRefGoogle Scholar
  20. Mei Y, Nikolopoulos E I, Anagnostou E N et al., 2016. Evaluating satellite precipitation error propagation in runoff simulations of mountainous basins. Journal of Hydrometeorology, 17: 1407–1423. doi: 10.1175/JHM-D-15-0081.1CrossRefGoogle Scholar
  21. Mekong River Commission, 2005. Overview of the Hydrology of the Mekong Basin. Vientiane: Mekong River Commission.Google Scholar
  22. Moriasi D N, Arnold J G, Van Liew M W et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the American Society of Agricultural and Biological Engineers, 50(3): 885–900. doi: 10.13031/2013.23153Google Scholar
  23. Mushore T D, Manatsa D, Pedzisai E et al., 2016. Investigating the implications of meteorological indicators of seasonal rainfall performance on maize yield in a rain-fed agricultural system: case study of Mt. Darwin District in Zimbabwe. Theoretical and Applied Climatology, 129(3): 1–7. doi: 10.1007/s00704-016-1838-2Google Scholar
  24. Neitsch S L, Arnold J G, Kiniry J R et al., 2011. Soil and Water Assessment Tool: Theoretical Documentation (Version 2009). Texas: Grassland, Soil and Water Research Laboratory, Agricultural Research Service, Blackland Research Center, Texas Agricultural Experiment Station.Google Scholar
  25. Nikolopoulos E I, Anagnostou E N, Borga M, 2013. Using highresolution satellite rainfall products to simulate a major flash flood event in northern Italy. Journal of Hydrometeorology, 14: 171–185. doi: 10.1175/JHM-D-12-09.1CrossRefGoogle Scholar
  26. Räsänen T A, Someth P, Lauri H et al., 2017. Observed river discharge changes due to hydropower operations in the Upper Mekong Basin. Journal of Hydrology, 545: 28–41. doi: 10.1016/j.jhydrol.2016.12.023CrossRefGoogle Scholar
  27. Rozante J R, Moreira D S, de Goncalves L G G et al., 2010. Combining TRMM and surface observations of precipitation: technique and validation over South America. Weather and Forecast, 25: 885–894. doi: 10.1175/2010WAF2222325.1CrossRefGoogle Scholar
  28. Sabo J L, Ruhi A, Holtgrieve G W et al., 2017. Designing river flows to improve food security futures in the Lower Mekong Basin. Science, 358: eaao1053. doi: 10.1126/science.aao1053CrossRefGoogle Scholar
  29. Serrat-Capdevila A, Valdes J B, Stakhiv E Z, 2014. Water management applications for satellite precipitation products: Synthesis and recommendations. Journal of the American Water Resources Association, 50(2): 509–525. doi: 10.1111/jawr.12140CrossRefGoogle Scholar
  30. Shrestha N K, Qamer F M, Pedreros D et al., 2017. Evaluating the accuracy of Climate Hazard Group (CHG) satellite rainfall estimates for precipitation based drought monitoring in Koshi basin, Nepal. Journal of Hydrology: Regional Studies, 13: 138–151. doi: 10.1016/j.ejrh.2017.08.004Google Scholar
  31. Son N T, Chen C F, Chen C R et al., 2012. Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data. International Journal of Applied Earth Observation and Geoinformation, 18: 417–427. doi: 10.1016/j.jag.2012.03.014CrossRefGoogle Scholar
  32. Tatsumi K, Yamashiki Y, 2015. Effect of irrigation water withdrawals on water and energy balance in the Mekong River Basin using an improved VIC land surface model with fewer calibration parameters. Agricultural Water Management, 159: 92–106. doi: 10.1016/j.agwat.2015.05.011CrossRefGoogle Scholar
  33. Thilakarathne M, Sridhar V, 2017. Characterization of future drought conditions in the Lower Mekong River Basin. Weather and Climate Extremes, 17: 47–58. doi: 10.1016/j.wace.2017.07.004CrossRefGoogle Scholar
  34. Thu H N, Wehn U, 2016. Data sharing in international transboundary contexts: the Vietnamese perspective on data sharing in the Lower Mekong Basin. Journal of Hydrology, 536: 351–364. doi: 10.1016/j.jhydrol.2016.02.035CrossRefGoogle Scholar
  35. Tobin K J, Bennett M E, 2013. Temporal analysis of Soil and Water Assessment Tool (SWAT) performance based on remotely sensed precipitation products. Hydrological Processes, 27: 505–514. doi: 10.1002/hyp.9252CrossRefGoogle Scholar
  36. Trejo F J P, Barbosa H A, Peñaloza-Murillo M A et al., 2016. Intercomparison of improved satellite rainfall estimation with CHIRPS gridded product and rain gauge data over Venezuela. Atmósfera, 29(4): 323–342. doi: 10.20937/ATM.2016.29.04.04CrossRefGoogle Scholar
  37. Tuo Y, Duan Z, Disse M et al., 2016. Evaluation of precipitation input for SWAT modeling in Alpine catchment: a case study in the Adige river basin (Italy). Science of the Total Environment, 573: 66–82. doi: 10.1016/j.scitotenv.2016.08.034CrossRefGoogle Scholar
  38. Wang W, Lu H, Yang D et al., 2016. Modeling hydrologic processes in the Mekong River Basin using a distributed model driven by satellite precipitation and rain gauge observations. PLoS ONE, 11(3): e0152229. doi:10.1371/journal.pone.0152229CrossRefGoogle Scholar
  39. Wang W, Lu H, Zhao T et al., 2017. Evaluation and comparison of daily rainfall from latest GPM and TRMM products over the Mekong River Basin. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99: 1–10. doi: 10.1109/JSTARS.2017.2672786Google Scholar
  40. Worqlul A W, Yen H, Collick A S et al., 2017. Evaluation of CFSR, TMPA 3B42 and ground-based rainfall data as input for hydrological models, in data-scarce regions: The upper Blue Nile Basin, Ethiopia. Catena, 152: 242–251. doi: 10.1016/j.catena.2017.01.019CrossRefGoogle Scholar
  41. Yong B, Liu D, Gourley J J et al., 2015. Global view of real-time TRMM multisatellite precipitation analysis: implications for its successor global precipitation measurement mission. Bulletin of the American Meteorological Society, 96: 283–296. doi: 10.1175/BAMS-D-14-00017.1CrossRefGoogle Scholar
  42. Zhou M C, Ishidaira H, Takeuchi K, 2008. Comparative study of potential evapotranspiration and interception evaporation by land cover over Mekong basin. Hydrological Processes, 22: 1290–1309. doi: 10.1002/hyp.6939CrossRefGoogle Scholar

Copyright information

© Science Press and Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xian Luo
    • 1
    • 2
  • Wenqi Wu
    • 1
  • Daming He
    • 1
    • 2
    Email author
  • Yungang Li
    • 1
    • 2
  • Xuan Ji
    • 1
    • 2
  1. 1.Asian International Rivers CenterYunnan UniversityKunmingChina
  2. 2.Yunnan Key Laboratory of International Rivers and Transboundary Eco-securityKunmingChina

Personalised recommendations