• Barros, A. P., M. Joshi, J. Putkonen, and D. W. Burbank, 2000: A study of the 1999 monsoon rainfall in a mountainous region in central Nepal using TRMM products and rain gauge observations. Geophys. Res. Lett., 27, 36833686, doi:10.1029/2000GL011827.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barros, A. P., and Coauthors, 2014: NASA GPM-Ground Validation: Integrated Precipitation and Hydrology Experiment 2014 science plan. NASA, 66 pp. [Available online at https://pmm.nasa.gov/iphex.]

    • Crossref
    • Export Citation
  • Brutsaert, W., 2005: Hydrology: An Introduction. Cambridge University Press, 605 pp.

  • Castro, L. M., M. Miranda, and B. Fernandez, 2015: Evaluation of TRMM Multi-satellite Precipitation Analysis (TMPA) in a mountainous region of the central Andes range with a Mediterranean climate. Hydrol. Res., 46, 89105, doi:10.2166/nh.2013.096.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S., and Coauthors, 2013: Evaluation of the successive V6 and V7 TRMM Multisatellite Precipitation Analysis over the continental United States. Water Resour. Res., 49, 81748186, doi:10.1002/2012WR012795.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Condom, T., P. Rau, and J. C. Espinoza, 2011: Correction of TRMM 3B43 monthly precipitation data over the mountainous areas of Peru during the period 1998–2007. Hydrol. Processes, 25, 19241933, doi:10.1002/hyp.7949.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan, 2002: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput., 6, 182197, doi:10.1109/4235.996017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dinku, T., P. Ceccato, E. Grover-Kopec, M. Lemma, S. J. Connor, and C. F. Ropelewski, 2007: Validation of satellite rainfall products over East Africa’s complex topography. Int. J. Remote Sens., 28, 15031526, doi:10.1080/01431160600954688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giovannettone, J. P., and A. P. Barros, 2009: Probing regional orographic controls of precipitation and cloudiness in the central Andes using satellite data. J. Hydrometeor., 10, 167182, doi:10.1175/2008JHM973.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harris, A., S. Rahman, F. Hossain, L. Yarborough, A. C. Bagtzoglou, and G. Easson, 2007: Satellite-based flood modeling using TRMM-based rainfall products. Sensors, 7, 34163427, doi:10.3390/s7123416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, Z. H., F. Q. Tian, H. V. Gupta, H. C. Hu, and H. P. Hu, 2015: Diagnostic calibration of a hydrological model in a mountain area by hydrograph partitioning. Hydrol. Earth Syst. Sci., 19, 18071826, doi:10.5194/hess-19-1807-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, Z. H., H. C. Hu, F. Q. Tian, G. H. Ni, and Q. F. Hu, 2017: Correcting the TRMM rainfall product for hydrological modelling in sparsely-gauged mountainous basins. Hydrol. Sci. J., 62, 306318, doi:10.1080/02626667.2016.1222532.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., G. Skofronick-Jackson, C. D. Kummerow, and J. M. Shepherd, 2008: Global Precipitation Measurement. Precipitation: Advances in Measurement, Estimation and Prediction, S. Michaelides, Ed., Springer, 131–169, doi:10.1007/978-3-540-77655-0_6.

    • Crossref
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement mission. Bull. Amer. Meteor. Soc., 95, 701722, doi:10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and D. T. Bolvin, 2015: TRMM and other data precipitation data set documentation. NASA TRMM Doc., 44 pp. [Available online at http://pmm.nasa.gov/sites/default/files/imce/3B42_3B43_doc_V7.pdf.]

  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H. F., 1987: Snow ablation modeling and its application to Qiedeke basin (in Chinese). J. Xinjiang Agric. Univ., 1, 6775.

  • Jiang, S. H., L. L. Ren, Y. Hong, B. Yong, X. L. Yang, F. Yuan, and M. W. Ma, 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. J. Hydrol., 452–453, 213225, doi:10.1016/j.jhydrol.2012.05.055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487503, doi:10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., and G. Huffman, 2011: Global Precipitation Measurement. Meteor. Appl., 18, 334353, doi:10.1002/met.284.

  • Kollat, J. B., and P. M. Reed, 2006: Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design. Adv. Water Resour., 29, 792807, doi:10.1016/j.advwatres.2005.07.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krakauer, N. Y., S. M. Pradhanag, T. Lakhankar, and A. K. Jha, 2013: Evaluating satellite products for precipitation estimation in mountain regions: A case study for Nepal. Remote Sens., 5, 41074123, doi:10.3390/rs5084107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Legates, D. R., and C. J. Willmott, 1990: Mean seasonal and spatial variability in gauge-corrected, global precipitation. Int. J. Climatol., 10, 111127, doi:10.1002/joc.3370100202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, J. Z., Z. Duan, J. C. Jiang, and A. X. Zhu, 2015: Evaluation of three satellite precipitation products TRMM 3B42, CMORPH, and PERSIANN over a subtropical watershed in China. Adv. Meteor., 2015, 151239, doi:10.1155/2015/151239.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Z., 2015: Comparison of precipitation estimates between version 7 3-hourly TRMM Multi-satellite Precipitation Analysis (TMPA) near-real-time and research products. Atmos. Res., 153, 119133, doi:10.1016/j.atmosres.2014.07.032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Z., 2016: Comparison of Integrated Multisatellite Retrievals for GPM (IMERG) and TRMM Multisatellite Precipitation Analysis (TMPA) monthly precipitation products: Initial results. J. Hydrometeor., 17, 777790, doi:10.1175/JHM-D-15-0068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Livneh, B., J. S. Deems, D. Schneider, J. J. Barsugli, and N. P. Molotch, 2014: Filling in the gaps: Inferring spatially distributed precipitation from gauge observations over complex terrain. Water Resour. Res., 50, 85898610, doi:10.1002/2014WR015442.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manh, N. V., N. V. Dung, N. N. Hung, M. Kummu, B. Merz, and H. Apel, 2015: Future sediment dynamics in the Mekong Delta floodplains: Impacts of hydropower development, climate change and sea level rise. Global Planet. Change, 127, 2233, doi:10.1016/j.gloplacha.2015.01.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mashingia, F., F. Mtalo, and M. Bruen, 2014: Validation of remotely sensed rainfall over major climatic regions in northeast Tanzania. Phys. Chem. Earth, 67–69, 5563, doi:10.1016/j.pce.2013.09.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meng, J., L. Li, Z. C. Hao, J. H. Wang, and Q. X. Shao, 2014: Suitability of TRMM satellite rainfall in driving a distributed hydrological model in the source region of Yellow River. J. Hydrol., 509, 320332, doi:10.1016/j.jhydrol.2013.11.049.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mu, Z. X., and H. F. Jiang, 2009: Establishment of snowmelt type Xin’anjiang watershed model based on digital elevation model (in Chinese). J. Xinjiang Agric. Univ., 5 (32), 7580.

    • Search Google Scholar
    • Export Citation
  • NASA, 2015: GPM/DPR level-3. Algorithm Theoretical Basis Doc., 12 pp. [Available online at https://pps.gsfc.nasa.gov/Documents/Level%203%20DPR%20ATBD.pdf.]

  • Nash, J. E., and J. V. Sutcliffe, 1970: River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol., 10, 282290, doi:10.1016/0022-1694(70)90255-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Negri, A. J., R. F. Adler, and L. Xu, 2002: A TRMM-calibrated infrared rainfall algorithm applied over Brazil. J. Geophys. Res., 107, 8048, doi:10.1029/2000JD000265, 2002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prakash, S., A. K. Mitra, D. S. Pai, and A. AghaKouchak, 2016: From TRMM to GPM: How well can heavy rainfall be detected from space? Adv. Water Resour., 88, 17, doi:10.1016/j.advwatres.2015.11.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salio, P., M. P. Hobouchian, Y. G. Skabar, and D. Vila, 2015: Evaluation of high-resolution satellite precipitation estimates over southern South America using a dense rain gauge network. Atmos. Res., 163, 146161, doi:10.1016/j.atmosres.2014.11.017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sapiano, M. R. P., and P. A. Arkin, 2009: An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J. Hydrometeor., 10, 149166, doi:10.1175/2008JHM1052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seto, S., T. Iguchi, and R. Meneghini, 2011: Comparison of TRMM PR V6 and V7 focusing heavy rainfall. 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 25822585, doi:10.1109/Igarss.2011.6049769.

    • Crossref
    • Export Citation
  • Shen, Y., A. Y. Xiong, Y. Wang, and P. P. Xie, 2010: Performance of high-resolution satellite precipitation products over China. J. Geophys. Res., 115, D02114, doi:10.1029/2009JD012097.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shi, W. L., X. Z. Yu, W. G. Liao, Y. Wang, and B. Z. Jia, 2013: Spatial and temporal variability of daily precipitation concentration in the Lancang River basin, China. J. Hydrol., 495, 197207, doi:10.1016/j.jhydrol.2013.05.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shige, S., H. Sasaki, K. Okamoto, and T. Iguchi, 2006: Validation of rainfall estimates from the TRMM Precipitation Radar and Microwave Imager using a radiative transfer model: 1. Comparison of the version-5 and -6 products. Geophys. Res. Lett., 33, L13803, doi:10.1029/2006GL026350.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sorooshian, S., K. L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 20352046, doi:10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, R. C., H. L. Yuan, X. L. Liu, and X. M. Jiang, 2016: Evaluation of the latest satellite–gauge precipitation products and their hydrologic applications over the Huaihe River basin. J. Hydrol., 536, 302319, doi:10.1016/j.jhydrol.2016.02.054.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, M. L., A. Ibrahim, Z. Duan, A. P. Cracknell, and V. Chaplot, 2015: Evaluation of six high-resolution satellite and ground-based precipitation products over Malaysia. Remote Sens., 7, 15041528, doi:10.3390/rs70201504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, G. Q., Y. Z. Ma, D. Long, L. Z. Zhong, and Y. Hong, 2016a: Evaluation of GPM Day-1 IMERG and TMPA version-7 legacy products over mainland China at multiple spatiotemporal scales. J. Hydrol., 533, 152167, doi:10.1016/j.jhydrol.2015.12.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, G. Q., Z. Y. Zeng, D. Long, X. L. Guo, B. Yong, W. H. Zhang, and Y. Hong, 2016b: Statistical and hydrological comparisons between TRMM and GPM level-3 products over a midlatitude basin: Is Day-1 IMERG a good successor for TMPA 3B42V7? J. Hydrometeor., 17, 121137, doi:10.1175/JHM-D-15-0059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tapiador, F. J., and Coauthors, 2012: Global Precipitation Measurement: Methods, datasets and applications. Atmos. Res., 104–105, 7097, doi:10.1016/j.atmosres.2011.10.021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, Y., Y. P. Xu, and X. J. Zhang, 2013: Assessment of climate change impacts on river high flows through comparative use of GR4J, HBV and Xinanjiang Models. Water Resour. Manage., 27, 28712888, doi:10.1007/s11269-013-0321-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tong, K., F. G. Su, D. Q. Yang, and Z. C. Hao, 2014: Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau. J. Hydrol., 519A, 423437, doi:10.1016/j.jhydrol.2014.07.044.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turk, F. J., G. V. Mostovoy, and V. G. Anantharaj, 2010: Soil moisture sensitivity to NRL-blend high-resolution precipitation products: Analysis of simulations with two land surface models. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 3, 3248, doi:10.1109/JSTARS.2009.2034024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ushio, T., and Coauthors, 2009: A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. J. Meteor. Soc. Japan, 87A, 137151, doi:10.2151/jmsj.87A.137.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wanders, N., M. Pan, and E. F. Wood, 2015: Correction of real-time satellite precipitation with multi-sensor satellite observations of land surface variables. Remote Sens. Environ., 160, 206221, doi:10.1016/j.rse.2015.01.016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X. L., and A. Lin, 2015: An algorithm for integrating satellite precipitation estimates with in situ precipitation data on a pentad time scale. J. Geophys. Res. Atmos., 120, 37283744, doi:10.1002/2014JD022788.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wolff, D. B., D. A. Marks, E. Amitai, D. S. Silberstein, B. L. Fisher, A. Tokay, J. Wang, and J. L. Pippitt, 2005: Ground validation for the Tropical Rainfall Measuring Mission (TRMM). J. Atmos. Oceanic Technol., 22, 365380, doi:10.1175/JTECH1700.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, S. G., C. Y. Wu, L. Wang, A. Gonsamo, Y. Shen, and Z. Niu, 2015: A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics. Remote Sens. Environ., 162, 119140, doi:10.1016/j.rse.2015.02.024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, X. W., Y. Hong, A. S. Limaye, J. J. Gourley, G. J. Huffman, S. I. Khan, C. Dorji, and S. Chen, 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? J. Hydrol., 499, 9199, doi:10.1016/j.jhydrol.2013.06.042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yin, Z. Y., X. Q. Zhang, X. D. Liu, M. Colella, and X. L. Chen, 2008: An assessment of the biases of satellite rainfall estimates over the Tibetan Plateau and correction methods based on topographic analysis. J. Hydrometeor., 9, 301326, doi:10.1175/2007JHM903.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yong, B., L. L. Ren, Y. Hong, J. H. Wang, J. J. Gourley, S. H. Jiang, X. Chen, and W. Wang, 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, W07542, doi:10.1029/2009WR008965.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yong, B., D. Liu, J. J. Gourley, Y. D. Tian, G. J. Huffman, L. L. Ren, and Y. Hong, 2015: Global view of real-time TRMM Multisatellite Precipitation Analysis implications for its successor Global Precipitation Measurement mission. Bull. Amer. Meteor. Soc., 96, 283296, doi:10.1175/BAMS-D-14-00017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zeng, H. W., L. J. Li, J. M. Hu, L. Q. Liang, J. Y. Li, B. Li, and K. Zhang, 2013: Accuracy validation of TRMM Multisatellite Precipitation Analysis daily precipitation products in the Lancang River basin of China. Theor. Appl. Climatol., 112, 389401, doi:10.1007/s00704-012-0733-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, S. F., D. Hua, X. J. Meng, and Y. Y. Zhang, 2011: Climate change and its driving effect on the runoff in the “Three-River Headwaters” region. J. Geogr. Sci., 21, 963978, doi:10.1007/s11442-011-0893-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, C. C., S. X. Yao, J. Wang, and J. B. Cui, 2013: Evaluation of satellite-based precipitation over Tianshan mountains. Proc. 2013 Int. Conf. on Remote Sensing, Environment and Transportation Engineering (RSETE 2013), Nanjing, China, Jiangsu Computer Society, 957960, doi:10.2991/rsete.2013.232.

    • Crossref
    • Export Citation
  • Zhao, R.-J., 1992: The Xinanjiang model applied in China. J. Hydrol., 135, 371381, doi:10.1016/0022-1694(92)90096-E.

  • Zhuo, L., D. W. Han, Q. Dai, T. Islam, and P. K. Srivastava, 2015: Appraisal of NLDAS-2 multi-model simulated soil moistures for hydrological modelling. Water Resour. Manage., 29, 35033517, doi:10.1007/s11269-015-1011-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Intercomparisons of Rainfall Estimates from TRMM and GPM Multisatellite Products over the Upper Mekong River Basin

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  • 1 Department of Hydraulic Engineering, Tsinghua University, Beijing, China
  • | 2 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey
  • | 3 Department of Hydraulic Engineering, Tsinghua University, Beijing, China
  • | 4 Hydrology Bureau, Ministry of Water Resources, Beijing, China
  • | 5 Ministry of Education Key Laboratory for Earth System Modeling, and Department of Earth System Science, Tsinghua University, and Joint Center for Global Change Studies, Beijing, China
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Abstract

The aim of this study is to evaluate the accuracy of daily rainfall estimates based on the GPM level-3 final product derived from the IMERG algorithm (abbreviated as IMERG) and TRMM 3B42, version 7 (abbreviated as 3B42), in the upper Mekong River basin, a mountainous region in southwestern China. High-density rain gauges provide exceptional resources for ground validation of satellite rainfall estimates over this region. The performance of the two satellite rainfall products is evaluated during two rainy seasons (May–October) over the period 2014–15, as well as their applications in hydrological simulations. Results indicate that 1) IMERG systematically reduces the bias value in rainfall estimates at the gridbox scale and presents a greater ability to capture rainfall variability at the local domain scale compared with 3B42; 2) IMERG improves the ability to capture rain events with moderate intensities and presents higher capability in detecting occurrences of extreme rain events, but significantly overestimates the amounts of these extreme events; and 3) IMERG generally produces comparable daily streamflow simulations to 3B42 and tends to outperform 3B42 in driving hydrological simulations when calibrating model parameters using each rainfall input. This study provides an early evaluation of the IMERG rainfall product over a mountainous region. The findings indicate the potential of the IMERG product in overestimating extreme rain events, which could serve as the basis for further improvement of IMERG rainfall retrieval algorithms. The hydrological evaluations described here could shed light on the emerging application of retrospectively generated IMERG products back to the TRMM era.

Current affiliation: Section 5.4 Hydrology, GFZ German Research Centre for Geosciences, Potsdam, Germany.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Fuqiang Tian, tianfq@mail.tsinghua.edu.cn

Abstract

The aim of this study is to evaluate the accuracy of daily rainfall estimates based on the GPM level-3 final product derived from the IMERG algorithm (abbreviated as IMERG) and TRMM 3B42, version 7 (abbreviated as 3B42), in the upper Mekong River basin, a mountainous region in southwestern China. High-density rain gauges provide exceptional resources for ground validation of satellite rainfall estimates over this region. The performance of the two satellite rainfall products is evaluated during two rainy seasons (May–October) over the period 2014–15, as well as their applications in hydrological simulations. Results indicate that 1) IMERG systematically reduces the bias value in rainfall estimates at the gridbox scale and presents a greater ability to capture rainfall variability at the local domain scale compared with 3B42; 2) IMERG improves the ability to capture rain events with moderate intensities and presents higher capability in detecting occurrences of extreme rain events, but significantly overestimates the amounts of these extreme events; and 3) IMERG generally produces comparable daily streamflow simulations to 3B42 and tends to outperform 3B42 in driving hydrological simulations when calibrating model parameters using each rainfall input. This study provides an early evaluation of the IMERG rainfall product over a mountainous region. The findings indicate the potential of the IMERG product in overestimating extreme rain events, which could serve as the basis for further improvement of IMERG rainfall retrieval algorithms. The hydrological evaluations described here could shed light on the emerging application of retrospectively generated IMERG products back to the TRMM era.

Current affiliation: Section 5.4 Hydrology, GFZ German Research Centre for Geosciences, Potsdam, Germany.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Fuqiang Tian, tianfq@mail.tsinghua.edu.cn
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