Advertisement

Theoretical and Applied Climatology

, Volume 135, Issue 1–2, pp 45–59 | Cite as

Spatial downscaling algorithm of TRMM precipitation based on multiple high-resolution satellite data for Inner Mongolia, China

  • Limin Duan
  • Keke Fan
  • Wei Li
  • Tingxi LiuEmail author
Original Paper

Abstract

Daily precipitation data from 42 stations in Inner Mongolia, China for the 10 years period from 1 January 2001 to 31 December 2010 was utilized along with downscaled data from the Tropical Rainfall Measuring Mission (TRMM) with a spatial resolution of 0.25° × 0.25° for the same period based on the statistical relationships between the normalized difference vegetation index (NDVI), meteorological variables, and digital elevation models (DEM) using the leave-one-out (LOO) cross validation method and multivariate step regression. The results indicate that (1) TRMM data can indeed be used to estimate annual precipitation in Inner Mongolia and there is a linear relationship between annual TRMM and observed precipitation; (2) there is a significant relationship between TRMM-based precipitation and predicted precipitation, with a spatial resolution of 0.50° × 0.50°; (3) NDVI and temperature are important factors influencing the downscaling of TRMM precipitation data for DEM and the slope is not the most significant factor affecting the downscaled TRMM data; and (4) the downscaled TRMM data reflects spatial patterns in annual precipitation reasonably well, showing less precipitation falling in west Inner Mongolia and more in the south and southeast. The new approach proposed here provides a useful alternative for evaluating spatial patterns in precipitation and can thus be applied to generate a more accurate precipitation dataset to support both irrigation management and the conservation of this fragile grassland ecosystem.

Notes

Acknowledgements

Our cordial thanks are extended to the editor, Prof. Dr. Dominique Ruffieux, and the journal’s anonymous reviewers for their professional and pertinent comments, which have greatly improved the quality of this manuscript.

Funding information

This study was financially supported by the following contracts: the Excellent Young Scientist Foundation of Inner Mongolia Agricultural University of China (Grant No.: 2014XYQ-11), the National Natural Science Foundation of China (Grant No.: 51509131, 51369016, 51620105003), the International S&T Cooperation Program of China (2015DFA00530), the Ministry of Education Innovative Research Team (Grant No.: IRT_17R60), the Natural Science Foundation of Inner Mongolia (Grant No.: 2015BS0514, 2015MS0566), the Innovation Team in Priority Areas Accredited by the Ministry of Science and Technology (Grant No.: 2015RA4013), and the National Science Foundation for Distinguished Young Scholars of China (Grant No.: 51425903).

References

  1. Agam N, Kustas WP, Anderson MC (2007) A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sens Environ 107(4):545–558.  https://doi.org/10.1016/j.rse.2006.10.006 CrossRefGoogle Scholar
  2. Akaike H (1987) Factor analysis and AIC. Psychometrika 52(3):317–332.  https://doi.org/10.1007/BF02294359 CrossRefGoogle Scholar
  3. Ba MB, Gruber A (2001) GOES multispectral rainfall algorithm (GMSRA). J Appl Meteorol 40(8):1500–1514.  https://doi.org/10.1175/1520-0450(2001)040<1500:GMRAG>2.0.CO;2 CrossRefGoogle Scholar
  4. Braswell BH, Schimel DS, Linder E, Moore B III (1997) The response of global terrestrial ecosystems to interannual temperature variability. Science 278(5339):870–873.  https://doi.org/10.1126/science.278.5339.870 CrossRefGoogle Scholar
  5. Buitenwerf R, Rose L, Higgins SI (2015) Three decades of multi-dimensional change in global leaf phenology. Nat Clim Chang 5(4):364–368.  https://doi.org/10.1038/nclimate2533 CrossRefGoogle Scholar
  6. Collischonn B, Collischonn W, Tucci CEM (2008) Daily hydrological modelling in the Amazon basin using TRMM rainfall estimates. J Hydrol 360(1–4):207–216.  https://doi.org/10.1016/j.jhydrol.2008.07.032 CrossRefGoogle Scholar
  7. Davenport ML, Nicholson SE (1993) On the relation between rainfall and the normalized difference vegetation index for diverse vegetation types in East Africa. Int J Remote Sens 14(12):2369–2389.  https://doi.org/10.1080/01431169308954042 CrossRefGoogle Scholar
  8. Dingman SL (2002) Physical hydrology. Prentice Hall, Boca RatonGoogle Scholar
  9. Du L, Tian Q, Yu T, Meng Q, Jancso T, Udvardy P, Huang Y (2013) A comprehensive drought monitoring method integrating MODIS and TRMM data. Int J Appl Earth Obs Geoinf 23:245–253.  https://doi.org/10.1016/j.jag.2012.09.010 CrossRefGoogle Scholar
  10. Duan Z, Bastiaanssen WGM (2013) First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling-calibration procedure. Remote Sens Environ 131:1–13.  https://doi.org/10.1016/j.rse.2012.12.002 CrossRefGoogle Scholar
  11. Fang J, Du J, Xu W, Shi P, Li M, Ming X (2013) Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area. Adv Water Resour 61:42–50.  https://doi.org/10.1016/j.advwatres.2013.08.011 CrossRefGoogle Scholar
  12. Ferraro RR (1997) Special sensor microwave imager derived global rainfall estimates for climatological applications. J Geophys Res Atmos 102(D14):16715–16735.  https://doi.org/10.1029/97JD01210 CrossRefGoogle Scholar
  13. Ghulam A, Ghulam O, Maimaitijiang M, Freeman K, Porton I, Maimaitiyiming M (2015) Remote sensing based spatial statistics to document tropical rainforest transition pathways. Remote Sens 7(5):6257–6279.  https://doi.org/10.3390/rs70506257 CrossRefGoogle Scholar
  14. Gong Z, Kawamura K, Ishikawa N, Goto M, Wulan T, Alateng D, Yin T, Ito Y (2015) MODIS normalized difference vegetation index (NDVI) and vegetation phenology dynamics in the Inner Mongolia grassland. Solid Earth 6(4):1185–1194.  https://doi.org/10.5194/se-6-1185-2015 CrossRefGoogle Scholar
  15. Guan H, Wilson JL, Xie H (2009) A cluster-optimizing regression-based approach for precipitation spatial downscaling in mountainous terrain. J Hydrol 375(3):578–588.  https://doi.org/10.1016/j.jhydrol.2009.07.007 CrossRefGoogle Scholar
  16. Guo JZ, Liang X, Leung LR (2004) Impacts of different precipitation data sources on water budgets. J Hydrol 298(1–4):311–334.  https://doi.org/10.1016/j.jhydrol.2003.08.020 CrossRefGoogle Scholar
  17. Hou AY, Kakar R, Neeck S, Azabarzin A, Kummerow C, Kojima M, Oki R, Nakamura K, Iguchi T (2013) The Global Precipitation Measurement (GPM) mission. Bull Am Meteorol Soc.  https://doi.org/10.1175/BAMS-D-13-00164 1 (e-view)
  18. Huffman GJ, Bolvin TD, Nelkin JE, Wolff BD, Adler FR, Gu G, Hong Y, Bowman PK, Stocker FE (2007) The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8(1):38–55.  https://doi.org/10.1175/JHM560.1 CrossRefGoogle Scholar
  19. Hughes DA, Smakhtin V (1996) Daily flow time series patching or extension: a spatial interpolation approach based on flow duration curves. Hydrol Sci J 41(6):851–871.  https://doi.org/10.1080/02626669609491555 CrossRefGoogle Scholar
  20. Immerzeel WW, Rutten MM, Droogers P (2009) Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula. Remote Sens Environ 113(2):362–370.  https://doi.org/10.1016/j.rse.2008.10.004 CrossRefGoogle Scholar
  21. Jia S, Zhu W, Lu A, Yan T (2011) A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China. Remote Sens Environ 115(12):3069–3079.  https://doi.org/10.1016/j.rse.2011.06.009 CrossRefGoogle Scholar
  22. 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):487–503.  https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2 CrossRefGoogle Scholar
  23. Khalili M (2017) An efficient statistical approach to multi-site downscaling of daily precipitation series in the context of climate change. Clim Dyn 49(7–8):2261–2278.  https://doi.org/10.1007/s00382-016-3443-6 CrossRefGoogle Scholar
  24. Kummerow C, Simpson J, Thiele O, Barnes W, Chang ATC, Stocker E, Adler RF, Hou A, Kakar R, Wentz F, Ashcroft P, Kozu T, Hong Y, Okamoto K, Iguchi T, Kuroiwa H, Im E, Haddad Z, Huffman G, Ferrier B, Olson WS, Zipser E, Smith EA, Wilheit TT, North G, Krishnamurti T, Nakamura K (2000) The status of the tropical rainfall measuring mission (TRMM) after two years in orbit. J Appl Meteorol 39(12):1965–1982.  https://doi.org/10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2 CrossRefGoogle Scholar
  25. Langella G, Basile A, Bonfante A, Terribile F (2010) High-resolution space–time rainfall analysis using integrated ANN inference systems. J Hydrol 387(3):328–342.  https://doi.org/10.1016/j.jhydrol.2010.04.027 CrossRefGoogle Scholar
  26. Liu H, Tian F, Hu HC, Hu HP, Sivapalan M (2013) Soil moisture controls on patterns of grass green-up in Inner Mongolia: an index based approach. Hydrol Earth Syst Sci 17(2):805–815.  https://doi.org/10.5194/hess-17-805-2013 CrossRefGoogle Scholar
  27. Maisongrande P, Duchemin B, Dedieu G (2004) VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products. Int J Remote Sens 25(1):9–14.  https://doi.org/10.1080/0143116031000115265 CrossRefGoogle Scholar
  28. Mantas VM, Liu Z, Caro C, Pereira AJSC (2015) Validation of TRMM multi-satellite precipitation analysis (TMPA) products in the Peruvian Andes. Atmos Res 163:132–145.  https://doi.org/10.1016/j.atmosres.2014.11.012 CrossRefGoogle Scholar
  29. Michaelides S, Levizzani V, Anagnostou E, Bauer P, Kasparis T, Lane JE (2009) Precipitation: measurement, remote sensing, climatology and modeling. Atmos Res 94(4):512–533.  https://doi.org/10.1016/j.atmosres.2009.08.017 CrossRefGoogle Scholar
  30. Moran PA (1950) Notes on continuous stochastic phenomena. Biometrika 37(1–2):17–23.  https://doi.org/10.1093/biomet/37.1-2.17 CrossRefGoogle Scholar
  31. Nicholson SE, Farrar TJ (1994) The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. I. NDVI response to rainfall. Remote Sens Environ 50(2):107–120.  https://doi.org/10.1016/0034-4257(94)90038-8 CrossRefGoogle Scholar
  32. Picard RR, Cook RD (1984) Cross-validation of regression models. J Am Stat Assoc 79(387):575–583.  https://doi.org/10.1080/01621459.1984.10478083 CrossRefGoogle Scholar
  33. Rahman H, Dedieu G (1994) SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. Remote Sens 15(1):123–143.  https://doi.org/10.1080/01431169408954055 CrossRefGoogle Scholar
  34. Sato T, Kimura F, Kitoh A (2007) Projection of global warming onto regional precipitation over Mongolia using a regional climate model. J Hydrol 333(1):144–154.  https://doi.org/10.1016/j.jhydrol.2006.07.023 CrossRefGoogle Scholar
  35. Smith MB, Koren VI, Zhang Z, Reed MS, Pan JJ, Moreda F (2004) Runoff response to spatial variability in precipitation: an analysis of observed data. J Hydrol 298(1):267–286.  https://doi.org/10.1016/j.jhydrol.2004.03.039 CrossRefGoogle Scholar
  36. Sorooshian S, Hsu K-L, Gao X, Gupta HV, Imam B, Braithwaite D (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull Am Meteorol Soc 81(9):2035–2046.  https://doi.org/10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2 CrossRefGoogle Scholar
  37. Sun Y, Guo P, Yan X, Zhao T (2010) Dynamics of vegetation cover and its relationship with climate change and human activities in Inner Mongolia. J Nat Resour 25(3):407–414 (In Chinese with English abstract)Google Scholar
  38. Tang L, Tian Y, Yan F, Habib E (2015) An improved procedure for the validation of satellite-based precipitation estimates. Atmos Res 163:61–73.  https://doi.org/10.1016/j.atmosres.2014.12.016 CrossRefGoogle Scholar
  39. Tong A, He Y (2013) Comparative analysis of SPOT, Landsat, MODIS, and AVHRR normalized difference vegetation index data on the estimation of leaf area index in a mixed grassland ecosystem. J Appl Remote Sens 7(1):073599  https://doi.org/10.1117/1.JRS.7.073599 CrossRefGoogle Scholar
  40. Ukkola AM, Prentice IC, Keenan TF, van Dijk AIJM, Viney NR, Myneni RB, Bi J (2015) Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nat Clim Chang 6(1):75–78.  https://doi.org/10.1038/NCLIMATE2831 CrossRefGoogle Scholar
  41. Vicente-Serrano SM, Gouveia C, Camarero JJ, Beguería S, Trigo R, López-Moreno JI, Azorín-Molina C, Pasho E, Lorenzo-Lacruz J, Revuelto J, Morán-Tejeda E, Sanchez-Lorenzo A (2013) Response of vegetation to drought time-scales across global land biomes. Proc Natl Acad Sci 110(1):52–57.  https://doi.org/10.1073/pnas.1207068110 CrossRefGoogle Scholar
  42. Wan Z, Dozier J (1996) A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans Geosci Remote Sens 34(4):892–905CrossRefGoogle Scholar
  43. Wan Z, Zhang Y, Zhang Q, Li ZL (2002) Validation of the land-surface temperature products retrieved from terra moderate resolution imaging spectroradiometer data. Remote Sens Environ 83(1):163–180.  https://doi.org/10.1016/S0034-4257(02)00093-7 CrossRefGoogle Scholar
  44. Wang X, Piao S, Ciais P, Li J, Friedlingstein P, Koven C, Chen A (2011) Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006. Proc Natl Acad Sci 108(4):1240–1245.  https://doi.org/10.1073/pnas.1014425108 CrossRefGoogle Scholar
  45. Wilheit TT (1986) Some comments on passive microwave measurement of rain. Bull Am Meteorol Soc 67(10):1226–1232.  https://doi.org/10.1175/1520-0477(1986)067<1226:SCOPMM>2.0.CO;2 CrossRefGoogle Scholar
  46. Yamaoka K, Nakagawa T, Uno T (1978) Application of Akaike's information criterion (AIC) in the evaluation of linear pharmacokinetic equations. J Pharmacokinet Biopharm 6(2):165–175.  https://doi.org/10.1007/BF01117450 CrossRefGoogle Scholar
  47. Zhang Q, Singh VP, Li J, Chen X (2011) Analysis of the periods of maximum consecutive wet days in China. J Geophys Res 116(D23):D23106.  https://doi.org/10.1029/2011JD016088 Google Scholar
  48. Zhang Q, Li J, Singh VP, Xiao M (2013) Spatio-temporal relations between temperature and precipitation regimes: implications for temperature-induced changes in the hydrological cycle. Glob Planet Chang 111:57–76.  https://doi.org/10.1016/j.gloplacha.2013.08.012 CrossRefGoogle Scholar
  49. Zhang Q, Xiao M, Li J, Singh VP, Wang Z (2014) Topography-based spatial patterns of precipitation extremes in the Poyang Lake basin, China: changing properties and causes. J Hydrol 512:229–239.  https://doi.org/10.1016/j.jhydrol.2014.03.010 CrossRefGoogle Scholar
  50. Zhang Q, Shi P, Singh VP, Fan K, Huang J (2017) Spatial downscaling of TRMM-based precipitation data using vegetative response in Xinjiang, China. Int J Climatol 37(10):3895–3909.  https://doi.org/10.1002/joc.4964 CrossRefGoogle Scholar
  51. Zhou LM, Tucker CJ, Kaufmann RK, Slayback D, Shabanov NV, Myneni RB (2001) Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J Geophys Res 106(D17):20069–20083.  https://doi.org/10.1029/2000JD000115 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2017

Authors and Affiliations

  1. 1.College of Water Conservancy and Civil EngineeringInner Mongolia Agricultural UniversityHohhotChina
  2. 2.Ministry of Education Key Laboratory of Environmental Change and Natural DisasterBeijing Normal UniversityBeijingChina
  3. 3.State Key Laboratory of Earth Surface Processes and Resource EcologyBeijing Normal UniversityBeijingChina
  4. 4.Academy of Disaster Reduction and Emergency ManagementBeijing Normal UniversityBeijingChina
  5. 5.Application Center for System TechnologiesFraunhofer IOSBIlmenauGermany

Personalised recommendations