Merging ground and satellite-based precipitation data sets for improved hydrological simulations in the Xijiang River basin of China

  • Tao Chen
  • Liliang RenEmail author
  • Fei Yuan
  • Tiantian Tang
  • Xiaoli Yang
  • Shanhu Jiang
  • Yi Liu
  • Chongxu Zhao
  • Limin Zhang
Original Paper


Watershed management, disaster warning, and hydrological modeling require accurate spatiotemporal precipitation data sets. This paper presents a comprehensive assessment of a gauge-satellite-based precipitation product that merges the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) satellite precipitation product (SPP) and ground precipitation data at 134 rain gauges in the Xijiang River basin, South China. Two regression-based schemes, principal component regression (PCR) and multiple linear regression (MLR), were used to combine the gauge-based precipitation data and PERSIANN-CDR SPP and were compared at daily and annual scales. Furthermore, a hydrological model Variable Infiltration Capacity was used to calculate streamflow and to evaluate the impact of four different precipitation interpolation methods on the results of the hydrological model at the daily scale. The result shows that the PCR method performs better than MLR and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. On the whole, the combined scheme consistently exhibits good performance and thus serves as a suitable tool for producing high-resolution gauge-and satellite-based precipitation datasets.


PERSIANN-CDR Precipitation data merge Principal component regression Multiple linear regression Hydrological model 



This study was sponsored by the National Key Research and Development Program (under Grant No. 2016YFA0601500) approved by the Ministry of Science and Technology of China, the National Natural Science Foundation of China (Grant Nos. 51779070 and 4173075), the National Natura Science Foundation of China (Grant No. 51579066), the Fundamental Research Funds for the Central Universities (Grant No. 2019B10414).


  1. Aghakouchak A, Nakhjiri N (2012) A near real-time satellite-based global drought climate data record. Environ Res Lett 7(4):1812–1818CrossRefGoogle Scholar
  2. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56: FAO. Rome 300(9):D05109Google Scholar
  3. Andreadis KM, Lettenmaier DP (2006) Trends in 20th century drought over the continental United States. Geophys Res Lett 13(10):10–1029Google Scholar
  4. Ashouri H, Hsu KL, Sorooshian S, Braithwaite DK, Knapp KR, Cecil LD, Nelson BR, Prat OP (2015) PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull Am Meteorol Soc 96(1):197–210CrossRefGoogle Scholar
  5. Babak O, Deutsch CV (2009) Statistical approach to inverse distance interpolation. Stoch Environ Res Risk Assess 23(5):543–553CrossRefGoogle Scholar
  6. Caracciolo D, Arnone E, Noto LV (2014) Influence of spatial precipitation sampling on hydrological response at the catchment scale. J Hydrol Eng 19(3):544–553CrossRefGoogle Scholar
  7. Chen T, Ren L, Yuan F, Yang X, Jiang S, Tang T, Liu Y, Zhao C, Zhang L (2017) Comparison of spatial interpolation schemes for rainfall data and application in hydrological modeling. Water 9(5):342CrossRefGoogle Scholar
  8. Chiang YM, Hsu KL, Chang FJ, Hong Y, Sorooshian S (2007) Merging multiple precipitation sources for flash flood forecasting. J Hydrol 340(3–4):183–196CrossRefGoogle Scholar
  9. Duan Q, Sorooshian S, Gupta V (1992) Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour Res 28(4):1015–1031CrossRefGoogle Scholar
  10. Duan QY, Gupta VK, Sorooshian S (1993) Shuffled complex evolution approach for effective and efficient global minimization. J Optim Theory Appl 76(3):501–521CrossRefGoogle Scholar
  11. Gebregiorgis A, Hossain F (2011) How much can a priori hydrologic model predictability help in optimal merging of satellite precipitation products? J Hydrometeorol 12(6):1287–1298CrossRefGoogle Scholar
  12. Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228(1–2):113–129CrossRefGoogle Scholar
  13. Haberlandt U (2007) Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event. J Hydrol 332(1–2):144–157CrossRefGoogle Scholar
  14. Hong Y, Hsu KL, Moradkhani H, Sorooshian S (2006) Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response. Water Resour Res 42(8):2643–2645CrossRefGoogle Scholar
  15. Hu Q, Yang H, Meng X, Wang Y, Deng P (2015) Satellite and gauge rainfall merging using geographically weighted regression. Proc Int Assoc Hydrol Sci 368(2015):132–137Google Scholar
  16. Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2010) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Springer, Netherlands, pp 3–22Google Scholar
  17. Hwang Y, Clark M, Rajagopalan B, Leavesley G (2012) Spatial interpolation schemes of daily precipitation for hydrologic modeling. Stoch Environ Res Risk Assess 26(2):295–320CrossRefGoogle Scholar
  18. José A, Filho P (2004) Integrating gauge, radar and satellite rainfall. In: 2nd Workshop of the International Precipitation Working GroupGoogle Scholar
  19. Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5(3):287–296CrossRefGoogle Scholar
  20. Kavetski D, Kuczera G, Franks SW (2006) Bayesian analysis of input uncertainty in hydrological modeling: 2. Application. Water Resour Res 42(3):W03407Google Scholar
  21. Kimani MW, Hoedjes JCB, Su Z (2017) An assessment of satellite-derived rainfall products relative to ground observations over East Africa. Remote Sens 9(5):430CrossRefGoogle Scholar
  22. Kubota T, Shige S, Hashizume H, Aonashi K, Takahashi N, Seto S, Takayabu YN, Ushio T, Nakagawa K, Iwanami K (2007) Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: production and validation. IEEE Trans Geosci Remote Sens 45(7):2259–2275CrossRefGoogle Scholar
  23. Kühnlein M, Appelhans T, Thies B, Nauss T (2014) Improving the accuracy of rainfall rates from optical satellite sensors with machine learning—a random forests-based approach applied to MSG SEVIRI. Remote Sens Environ 141(141):129–143CrossRefGoogle Scholar
  24. Kummerow C, Simpson J, Thiele O, Barnes W, Chang ATC, Stocker E, Adler RF, Hou A, Kakar R, Wentz F (2000) The status of the tropical rainfall measuring mission (TRMM) after two years in Orbit. J Appl Meteorol 39(12):1965–1982CrossRefGoogle Scholar
  25. Li M, Shao QX (2010) An improved statistical approach to merge satellite rainfall estimates and raingauge data. J Hydrol 385(1):51–64CrossRefGoogle Scholar
  26. Li M, Shao Q, Renzullo L (2010) Estimation and spatial interpolation of rainfall intensity distribution from the effective rate of precipitation. Stoch Environ Res Risk Assess 24(1):117–130CrossRefGoogle Scholar
  27. Liang X, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res Atmos 99(D7):14415–14428CrossRefGoogle Scholar
  28. Liang X, Lettenmaier DP, Wood EF (1996) One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model. J Geophys Res Atmos 101(D16):21403–21422CrossRefGoogle Scholar
  29. Maidment RI, Grimes D, Black E, Tarnavsky E, Young M, Greatrex H, Allan RP, Stein T, Nkonde E, Senkunda S (2017) A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa. Sci Data 4:170063CrossRefGoogle Scholar
  30. Marani M (2005) Correction to “Non-power-law-scale properties of rainfall in space and time”. Water Resour Res 41(8):323–333CrossRefGoogle Scholar
  31. Massy W (1965) Principal components regression in exploratory statistical research. Publ Am Stat Assoc 60(309):234–256CrossRefGoogle Scholar
  32. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290CrossRefGoogle Scholar
  33. Nijssen B, Shukla S, Lin C, Gao H, Zhou T, Sheffield T, Ishottama J, Wood EF, Lettenmaier DP (2014) A prototype global drought information system based on multiple land surface models. J Hydrometeorol 15(15):1661–1676CrossRefGoogle Scholar
  34. Obled C, Wendling J, Beven K (1994) The sensitivity of hydrological models to spatial rainfall patterns: an evaluation using observed data. J Hydrol 159(94):305–333CrossRefGoogle Scholar
  35. Plouffe CCF, Robertson C, Chandrapala L (2015) Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: a case study of Sri Lanka. Environ Model Softw 67(C):57–71CrossRefGoogle Scholar
  36. Rawls WJ, Ahuja LR, Brakensiek DL, Shirmohammadi A (2014) Handbook of hydrology. McGraw-Hill, New York, p 659Google Scholar
  37. Renard B, Kavetski D, Kuczera G, Thyer M, Franks SW (2010) Understanding predictive uncertainty in hydrologic modeling: the challenge of identifying input and structural errors. Water Resour Res 46(5):1187–1191CrossRefGoogle Scholar
  38. Rocha H, Li W, Hahn A (2006) Principal component regression using radial basis function interpolation. Wavel Splines Athens, Mod. Methods Math., Nashboro Press, Brentwood, pp 402–415Google Scholar
  39. Rodriguez-Iturbe I, Marani M, D’Odorico P, Rinaldo A (1998) On space-time scaling of cumulated rainfall fields. Water Resour Res 34(12):3461–3469CrossRefGoogle Scholar
  40. Rozante JR, Moreira DS, De Goncalves LGG, Vila DA (2010) Combining TRMM and surface observations of precipitation: technique and validation over South America. Weather Forecast 25(3):885–894CrossRefGoogle Scholar
  41. Seo D-J (1998) Real-time estimation of rainfall fields using radar rainfall and rain gage data. J Hydrol 208(1–2):37–52CrossRefGoogle Scholar
  42. Sheffield J, Andreadis KM, Wood EF, Lettenmaier DP (2009) Global and continental drought in the second half of the twentieth century: severity-area-duration analysis and temporal variability of large-scale events. J Clim 22(8):1962–1981CrossRefGoogle Scholar
  43. 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 Am Meteorol Soc 95(6):861–882CrossRefGoogle Scholar
  44. Shukla S, Steinemann AC, Lettenmaier DP (2011) Drought monitoring for Washington state: indicators and applications. J Hydrometeorol 12(1):66–83CrossRefGoogle Scholar
  45. Sinclair S, Pegram G (2010) Combining radar and rain gauge rainfall estimates using conditional merging. Atmos Sci Lett 6(1):19–22CrossRefGoogle Scholar
  46. Tian YD, Peterslidard CD, Eylander JB (2010) Real-time bias reduction for satellite-based precipitation estimates. J Hydrometeorol 11(6):1275–1285CrossRefGoogle Scholar
  47. Tobin KJ, Bennett ME (2010) Adjusting satellite precipitation data to facilitate hydrologic modeling. J Hydrometeorol 11(4):966–978CrossRefGoogle Scholar
  48. Todini E (2001) A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements. Hydrol Earth Syst Sci 5(2):106–108Google Scholar
  49. Vila DA, Goncalves LGGD, Toll DL, Rozante JR (2008) Statistical evaluation of combined daily gauge observations and rainfall satellite estimates over continental South America. J Hydrometeorol 10(2):533–543CrossRefGoogle Scholar
  50. Vrieling A, Sterk G, Jong SMD (2010) Satellite-based estimation of rainfall erosivity for Africa. J Hydrol 395(3):235–241CrossRefGoogle Scholar
  51. Wagner PD, Fiener P, Wilken F, Kumar S, Schneider K (2012) Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J Hydrol 464–465(5):388–400CrossRefGoogle Scholar
  52. Wanders N, Pan M, Wood EF (2015) Correction of real-time satellite precipitation with multi-sensor satellite observations of land surface variables. Remote Sens Environ 160:206–221CrossRefGoogle Scholar
  53. Wang A, Lettenmaier DP, Sheffield J (2011) Soil moisture drought in China, 1950–2006. J Clim 24(13):3257–3271CrossRefGoogle Scholar
  54. Wu Z, Xu Z, Fang W, Hai H, Zhou J, Wu X, Liu Z (2018) Hydrologic evaluation of multi-source satellite precipitation products for the Upper Huaihe River Basin China. Remote Sens 10(6):840CrossRefGoogle Scholar
  55. Xu H, Xu CY, Chen H, Zhang Z, Li L (2013) Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China. J Hydrol 505(2.96):1–12CrossRefGoogle Scholar
  56. Xu H, Xu CY, Sælthun NR, Zhou B, Xu Y (2015) Evaluation of reanalysis and satellite-based precipitation datasets in driving hydrological models in a humid region of southern china. Stoch Environ Res Risk Assess 29(8):2003–2020CrossRefGoogle Scholar
  57. Yong B, Ren LL, Hong Y, Wang JH, Gourley JJ, Jiang SH, Chen X, Wang W (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 Resour Res 46(7):759–768CrossRefGoogle Scholar
  58. Yuan F, Tung YK, Ren L (2016) Projection of future streamflow changes of the Pearl River basin in China using two delta-change methods. Hydrol Res 47(1):159Google Scholar
  59. Yuan F, Zhao C, Jiang Y, Ren L, Shan H, Zhang L, Zhu Y, Chen T, Jiang S, Yang X (2017) Evaluation on uncertainty sources in projecting hydrological changes over the Xijiang River basin in South China. J Hydrol 554:434–450CrossRefGoogle Scholar
  60. Zhang X, Tang Q (2015) Combining satellite precipitation and long-term ground observations for hydrological monitoring in China. J Geophys Res Atmos 120(13):6426–6443CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Tao Chen
    • 1
    • 2
  • Liliang Ren
    • 1
    • 2
    Email author
  • Fei Yuan
    • 1
    • 2
  • Tiantian Tang
    • 2
  • Xiaoli Yang
    • 2
  • Shanhu Jiang
    • 2
  • Yi Liu
    • 2
  • Chongxu Zhao
    • 1
    • 2
  • Limin Zhang
    • 1
    • 2
  1. 1.State Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringHohai UniversityNanjingChina
  2. 2.College of Hydrology and Water ResourcesHohai UniversityNanjingChina

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