• Adam, J. C., E. A. Clark, and D. P. Lettenmaie, 2006: Correction of global precipitation products for orographic effects. J. Climate, 19, 1538, doi:10.1175/JCLI3604.1.

    • Search Google Scholar
    • Export Citation
  • Alfieri, L., P. Burek, E. Dutra, B. Krzeminski, D. Muraro, J. Thielen, and F. Pappenberger, 2013: GloFAS—Global ensemble streamflow forecasting and flood early warning. Hydrol. Earth Syst. Sci., 17, 11611175, doi:10.5194/hess-17-1161-2013.

    • Search Google Scholar
    • Export Citation
  • Artan, G., H. Gadain, J. L. Smith, K. Asante, C. J. Bandaragoda, and J. P. Verdin, 2007: Adequacy of satellite derived rainfall data for streamflow modeling. Nat. Hazards, 43, 167185, doi:10.1007/s11069-007-9121-6.

    • Search Google Scholar
    • Export Citation
  • Baldwin, M. E., and K. E. Mitchell, 1998: Progress on the NCEP hourly multi-sensor U.S. precipitation analysis for operations and GCIP research. Preprints, Second Symp. on Integrated Observing Systems, Phoenix, AZ, Amer. Meteor. Soc., 1011.

  • Behrangi A., B. Khakbaz, T. C. Jaw, A. AghaKouchak, K. Hsu, and S. Sorooshian, 2011: Hydrologic evaluation of satellite precipitation products over a mid-size basin. J. Hydrol., 397, 225237, doi:10.1016/j.jhydrol.2010.11.043.

    • Search Google Scholar
    • Export Citation
  • Bell, T. L., and P. K. Kundu, 2000: Dependence of satellite sampling error on monthly averaged rain rates: Comparison of simple models and recent studies. J. Climate, 13, 449462, doi:10.1175/1520-0442(2000)013<0449:DOSSEO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Biemans, H., R. W. Hutjes, P. Kabat, B. J. Strengers, D. Gerten, and S. Rost, 2009: Effects of precipitation uncertainty on discharge calculations for main river basins. J. Hydrometeor., 10, 10111025, doi:10.1175/2008JHM1067.1.

    • Search Google Scholar
    • Export Citation
  • Bitew, M. M., and M. Gebremichael, 2011a: Evaluation of satellite rainfall products through hydrologic simulation in a fully distributed hydrologic model. Water Resour. Res., 47, W06526, doi:10.1029/2010WR009917.

    • Search Google Scholar
    • Export Citation
  • Bitew, M. M., and M. Gebremichael, 2011b: Assessment of satellite rainfall products for streamflow simulation in medium watersheds of the Ethiopian highlands. Hydrol. Earth Syst. Sci., 15, 11471155, doi:10.5194/hess-15-1147-2011.

    • Search Google Scholar
    • Export Citation
  • Bitew, M. M., M. Gebremichael, L. T. Ghebremichael, and Y. A. Bayissa, 2012: Evaluation of high-resolution satellite rainfall products through streamflow simulation in a hydrological modeling of a small mountainous watershed in Ethiopia. J. Hydrometeor., 13, 338350, doi:10.1175/2011JHM1292.1.

    • Search Google Scholar
    • Export Citation
  • Boone, A., and Coauthors, 2004: The Rhône-Aggregation Land Surface Scheme intercomparison project: An overview. J. Climate, 17, 187208, doi:10.1175/1520-0442(2004)017<0187:TRLSSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. Wayne Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, doi:10.1029/2007JD009132.

    • Search Google Scholar
    • Export Citation
  • Cherkauer, K. A., and D. P. Lettenmaier, 1999: Hydrologic effects of frozen soils in the upper Mississippi River basin. J. Geophys. Res., 104, 19 59919 610, doi:10.1029/1999JD900337.

    • Search Google Scholar
    • Export Citation
  • Christensen, N. S., A. W. Wood, N. Voisin, D. P. Lettenmaier, and R. N. Palmer, 2004: Effects of climate change on the hydrology and water resources of the Colorado River basin. Climatic Change, 62, 337363, doi:10.1023/B:CLIM.0000013684.13621.1f.

    • Search Google Scholar
    • Export Citation
  • Ciach, G. J., 2003: Local random errors in tipping-bucket rain gauge measurements. J. Atmos. Oceanic Technol., 20, 752759, doi:10.1175/1520-0426(2003)20<752:LREITB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Clark, M. P., and A. G. Slater, 2006: Probabilistic quantitative precipitation estimation in complex terrain. J. Hydrometeor., 7, 322, doi:10.1175/JHM474.1.

    • Search Google Scholar
    • Export Citation
  • Decharme, B., and H. Douville, 2006: Uncertainties in the GSWP-2 precipitation forcing and their impacts on regional and global hydrological simulations. Climate Dyn., 27, 695713, doi:10.1007/s00382-006-0160-6.

    • Search Google Scholar
    • Export Citation
  • Decharme, B., R. Alkama, F. Papa, S. Faroux, H. Douville, and C. Prigent, 2012: Global off-line evaluation of the ISBA-TRIP flood model. Climate Dyn., 38, 13891412, doi:10.1007/s00382-011-1054-9.

    • Search Google Scholar
    • Export Citation
  • Elsner, M. M., and Coauthors, 2010: Implications of 21st century climate change for the hydrology of Washington State. Climatic Change, 102, 225260, doi:10.1007/s10584-010-9855-0.

    • Search Google Scholar
    • Export Citation
  • Fekete, B. M., C. J. Vörösmarty, J. O. Road, and C. J. Willmott, 2004: Uncertainties in precipitation and their impacts on runoff estimates. J. Climate, 17, 294304, doi:10.1175/1520-0442(2004)017<0294:UIPATI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hamlet, A. F., and D. P. Lettenmaier, 2007: Effects of 20th century warming and climate variability on flood risk in the western U.S. Water Resour. Res., 43, W06427, doi:10.1029/2006WR005099.

    • Search Google Scholar
    • Export Citation
  • Hamlet, A. F., S. Y. Lee, K. E. B. Mickleson, and M. M. Elsner, 2010: Effects of projected climate change on energy supply and demand in the Pacific Northwest and Washington State. Climatic Change, 102, 103–128, doi:10.1007/s10584-010-9857-y.

    • Search Google Scholar
    • Export Citation
  • Hong, Y., R. F. Adler, F. Hossain, S. Curtis, and G. J. Huffman, 2007: A first approach to global runoff simulation using satellite rainfall estimation. Water Resour. Res., 43, W08502, doi:10.1029/2006WR005739.

    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement mission. Bull. Amer. Meteor. Soc., 95, 701–722, doi:10.1175/BAMS-D-13-00164.1.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., D. T. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, C. Kidd, E. J. Nelkin, and P. Xie, 2015: NASA Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Doc., version 4.5, 30 pp. [Available online at http://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V4.5.pdf.]

  • 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.

    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., J. E. Janowiak, P. A. Arkin, and 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.

    • Search Google Scholar
    • Export Citation
  • Kidd, C., and V. Levizzani, 2011: Status of satellite precipitation retrievals. Hydrol. Earth Syst. Sci., 15, 11091116, doi:10.5194/hess-15-1109-2011.

    • Search Google Scholar
    • Export Citation
  • Kirschbaum, D. B., and Coauthors, 2017: NASA’s remotely sensed precipitation: A reservoir for applications users. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-15-00296.1, in press.

    • Search Google Scholar
    • Export Citation
  • Kobold, M., and K. Su, 2005: Precipitation forecasts and their uncertainty as input into hydrological models precipitation forecasts and their uncertainty as input into hydrological models hydrological modeling in Slovenia. Hydrol. Earth Syst. Sci., 9, 322332, doi:10.5194/hess-9-322-2005.

    • Search Google Scholar
    • Export Citation
  • Komma, J., C. Reszler, G. Blöschl, and T. Haiden, 2007: Ensemble prediction of floods—Catchment non-linearity and forecast probabilities. Nat. Hazards Earth Syst. Sci., 7, 431444, doi:10.5194/nhess-7-431-2007.

    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., and R. Mantilla, 2010: Why where the 2008 floods so large? A Watershed Year: Anatomy of the Iowa Floods of 2008, C. F. Mutel, Ed., University of Iowa Press, 19–30.

  • Kuligowski, R. J., Y. Li, and Y. Zhang, 2013: Impact of TRMM data on a low-latency, high-resolution precipitation algorithm for flash-flood forecasting. J. Appl. Meteor. Climatol., 52, 13791393, doi:10.1175/JAMC-D-12-0107.1.

    • Search Google Scholar
    • Export Citation
  • Lehner, B., K. Verdin, and A. Jarvis, 2008: New global hydrography derived from spaceborne elevation data. Eos, Trans. Amer. Geophys. Union, 89, 9394, doi:10.1029/2008EO100001.

    • Search Google Scholar
    • Export Citation
  • Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A simple hydrologically based model of land surface water and energy fluxes for GSMs. J. Geophys. Res., 99, 14 41514 428, doi:10.1029/94JD00483.

    • Search Google Scholar
    • Export Citation
  • Liang, X., E. F. Wood, and D. P. Lettenmaier, 1996: Surface soil moisture parameterization of the VIC-2L model: Evaluation and modifications. Global Planet. Change, 13, 195206, doi:10.1016/0921-8181(95)00046-1.

    • Search Google Scholar
    • Export Citation
  • Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.]

  • Mitchell, K. E., and Coauthors, 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109, D07S90, doi:10.1029/2003JD003823.

    • Search Google Scholar
    • Export Citation
  • Mu, Q., M. Zhao, and S. W. Running, 2011: Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ., 115, 17811800, doi:10.1016/j.rse.2011.02.019.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • Nijssen, B., and D. Lettenmaier, 2004: Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites. J. Geophys. Res., 109, D02103, doi:10.1029/2003JD003497.

    • Search Google Scholar
    • Export Citation
  • Nikolopoulos, E. I., E. N. Anagnostou, and M. Borga, 2013: Using high-resolution satellite rainfall products to simulate a major flash flood event in northern Italy. J. Hydrometeor., 14, 171185, doi:10.1175/JHM-D-12-09.1.

    • Search Google Scholar
    • Export Citation
  • Oberg, K., and D. S. Mueller, 2007: Validation of streamflow measurements made with acoustic Doppler current profilers. J. Hydraul. Eng., 133, 14211432, doi:10.1061/(ASCE)0733-9429(2007)133:12(1421).

    • Search Google Scholar
    • Export Citation
  • Pan, M., H. Li, and E. Wood, 2010: Assessing the skill of satellite-based precipitation estimates in hydrologic applications. Water Resour. Res., 46, W09535, doi:10.1029/2009WR008290.

    • Search Google Scholar
    • Export Citation
  • Peters-Lidard, C. D., S. V. Kumar, D. M. Mocko, and Y. Tian, 2011: Estimating evapotranspiration with land data assimilation systems. Hydrol. Processes, 25, 39793992, doi:10.1002/hyp.8387.

    • Search Google Scholar
    • Export Citation
  • Pitlo, R. H., 1982: Flow resistance of aquatic vegetation. Proc. Sixth EWRS Symp. on Aquatic Weeds, Doorwerth, Netherlands, European Weed Research Society, 255234.

  • Powell, K. E. C., 1978: Weed growth: A factor in channel roughness. Hydrometry: Principles and Practices, R. W. Herschy, Ed., Wiley, 327–352.

  • Refsgaard, J. C., J. P. van der Sluijs, J. Brown, and P. van der Keur, 2006: A framework for dealing with uncertainty due to model structure error. Adv. Water Resour., 29, 15861597, doi:10.1016/j.advwatres.2005.11.013.

    • Search Google Scholar
    • Export Citation
  • Renard, B., D. Kavetski, E. Leblois, M. Thyer, G. Kuczera, and S. W. Franks, 2011: Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation. Water Resour. Res., 47, W11516, doi:10.1029/2011WR010643.

    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, doi:10.1175/JCLI-D-11-00015.1.

    • Search Google Scholar
    • Export Citation
  • Seo, B.-C., L. K. Cunha, and W. F. Krajewski, 2013: Uncertainty in radar–rainfall composite and its impact on hydrologic prediction for the eastern Iowa flood of 2008. Water Resour. Res., 49, 27472764, doi:10.1002/wrcr.20244.

    • Search Google Scholar
    • Export Citation
  • Smith, M. B., V. I. Koren, Z. Zhang, S. M. Reed, J.-J. Pan, and F. Moreda, 2004: Runoff response to spatial variability in precipitation: an analysis of observed data. J. Hydrol., 298, 267286, doi:10.1016/j.jhydrol.2004.03.039.

    • Search Google Scholar
    • Export Citation
  • Soong, D. T., C. D. Prater, T. M. Halfar, and L. A. Wobig, 2012: Manning’s roughness coefficient for Illinois streams. USGS Data Series 668, 22 pp. [Available online at https://pubs.usgs.gov/ds/668/pdf/DataSeries_668_2.pdf.]

  • Storck, P., D. P. Lettenmaier, and S. Bolton, 2002: Measurement of snow interception and canopy effects on snow accumulation and melt in a mountainous maritime climate, Oregon, United States. Water Resour. Res., 38, 1223, doi:10.1029/2002WR001281.

    • Search Google Scholar
    • Export Citation
  • Strahler, A. N., 1957: Quantitative analysis of watershed geomorphology. Trans. Amer. Geophys. Union, 38 (6), 913920, doi:10.1029/TR038i006p00913.

    • Search Google Scholar
    • Export Citation
  • Su, F. G., H. Gao, G. J. Huffman, and D. P. Lettenmaier, 2011: Potential utility of the real-time TMPA-RT precipitation estimates in streamflow prediction. J. Hydrometeor., 12, 444455, doi:10.1175/2010JHM1353.1.

    • Search Google Scholar
    • Export Citation
  • Tian, X., A. Dai, D. Yang, and Z. Xie, 2007: Effects of precipitation-bias corrections on surface hydrology over northern latitudes. J. Geophys. Res., 112, D14101, doi:10.1029/2007JD008420.

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

    • Search Google Scholar
    • Export Citation
  • Villarini, G., and W. F. Krajewski, 2010: Review of the different sources of uncertainty in single-polarization radar-based estimates of rainfall. Surv. Geophys., 31, 107129, doi:10.1007/s10712-009-9079-x.

    • Search Google Scholar
    • Export Citation
  • Voisin, N., A. W. Wood, and D. P. Lettenmaier, 2008: Evaluation of precipitation products for global hydrological prediction. J. Hydrometeor., 9, 388407, doi:10.1175/2007JHM938.1.

    • Search Google Scholar
    • Export Citation
  • Volkmann, T. H. M., S. W. Lyon, H. V. Gupta, and P. A. Troch, 2010: Multicriteria design of rain gauge networks for flash flood prediction in semiarid catchments with complex terrain. Water Resour. Res., 46, W11554, doi:10.1029/2010WR009145.

    • Search Google Scholar
    • Export Citation
  • Wu, H., J. S. Kimball, N. Mantua, and J. Stanford, 2011: Automated upscaling of river networks for macroscale hydrological modeling. Water Resour. Res., 47, W03517, doi:10.1029/2009WR008871.

    • Search Google Scholar
    • Export Citation
  • Wu, H., R. F. Adler, Y. Hong, Y. Tian, and F. Policelli, 2012a: Evaluation of global flood detection using satellite-based rainfall and a hydrologic model. J. Hydrometeor., 13, 12681284, doi:10.1175/JHM-D-11-087.1.

    • Search Google Scholar
    • Export Citation
  • Wu, H., J. S. Kimball, M. M. Elsner, N. Mantua, R. F. Adler, and J. Stanford, 2012b: Projected climate change impacts on the hydrology and temperature of Pacific Northwest rivers. Water Resour. Res., 48, W11530, doi:10.1029/2012WR012082.

    • Search Google Scholar
    • Export Citation
  • Wu, H., J. S. Kimball, H. Li, M. Huang, L. R. Leung, and R. F. Adler, 2012c: A new global river network database for macroscale hydrologic modeling. Water Resour. Res., 48, W09701, doi:10.1029/2012WR012313.

    • Search Google Scholar
    • Export Citation
  • Wu, H., R. F. Adler, Y. Tian, G. J. Huffman, H. Li, and J. Wang, 2014: Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model. Water Resour. Res., 50, 2693–2717, doi:10.1002/2013WR014710.

  • Xie, P., M. Chen, S. Yang, A. Yatagai, T. Hayasaka, Y. Fukushima, and C. Liu, 2007: A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeor., 8, 607–626, doi:10.1175/JHM583.1.

  • Zhang, J., and Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 13211338, doi:10.1175/2011BAMS-D-11-00047.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, K., J. S. Kimball, R. R. Nemani, and S. W. Running, 2010: A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006. Water Resour. Res., 46, W09522, doi:10.1029/2009WR008800.

    • Search Google Scholar
    • Export Citation
  • Zhao, R. J., and X. R. Liu, 1995: The Xinanjiang model. Computer Models of Watershed Hydrology, V. P. Singh, Ed., Water Resources Publications, 215–232.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 379 224 0
PDF Downloads 260 135 0

Evaluation of Quantitative Precipitation Estimations through Hydrological Modeling in IFloodS River Basins

View More View Less
  • 1 School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland
  • | 2 Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland
  • | 3 NASA Goddard Space Flight Center, Greenbelt, Maryland
Restricted access

Abstract

A multiple-product-driven hydrologic modeling framework (MMF) is utilized for evaluation of quantitative precipitation estimation (QPE) products, motivated by improving the utility of satellite QPE in global flood modeling. This work addresses the challenge of objectively determining the relative value of various QPEs at river basin/subbasin scales. A reference precipitation dataset is created using a long-term water-balance approach with independent data sources. The intercomparison of nine QPEs and corresponding hydrologic simulations indicates that all products with long-term (2002–13) records have similar merits as over the short-term (April–June 2013) Iowa Flood Studies period. The model performance in calculated streamflow varies approximately linearly with precipitation bias, demonstrating that the model successfully translated the level of precipitation quality to streamflow quality with better streamflow simulations from QPEs with less bias. Phase 2 of the North American Land Data Assimilation System (NLDAS-2) has the best streamflow results for the Iowa–Cedar River basin, with daily and monthly Nash–Sutcliffe coefficients and mean annual bias of 0.81, 0.88, and −2.1%, respectively, for the long-term period. The evaluation also indicates that a further adjustment of NLDAS-2 to form the best precipitation estimation should consider spatial–temporal distribution of bias. The satellite-only products have lower performance (peak and timing) than other products, while simple bias adjustment can intermediately improve the quality of simulated streamflow. The TMPA research product (TMPA-RP; research-quality data) can generate results approaching those of the ground-based products with only monthly gauge-based adjustment to the TMPA real-time product (TMPA-RT; near-real-time data). It is further noted that the streamflow bias is strongly correlated to precipitation bias at various time scales, though other factors may play a role as well, especially on the daily time scale.

© 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: Huan Wu, huanwu@umd.edu

This article is included in the IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission Special Collection.

Abstract

A multiple-product-driven hydrologic modeling framework (MMF) is utilized for evaluation of quantitative precipitation estimation (QPE) products, motivated by improving the utility of satellite QPE in global flood modeling. This work addresses the challenge of objectively determining the relative value of various QPEs at river basin/subbasin scales. A reference precipitation dataset is created using a long-term water-balance approach with independent data sources. The intercomparison of nine QPEs and corresponding hydrologic simulations indicates that all products with long-term (2002–13) records have similar merits as over the short-term (April–June 2013) Iowa Flood Studies period. The model performance in calculated streamflow varies approximately linearly with precipitation bias, demonstrating that the model successfully translated the level of precipitation quality to streamflow quality with better streamflow simulations from QPEs with less bias. Phase 2 of the North American Land Data Assimilation System (NLDAS-2) has the best streamflow results for the Iowa–Cedar River basin, with daily and monthly Nash–Sutcliffe coefficients and mean annual bias of 0.81, 0.88, and −2.1%, respectively, for the long-term period. The evaluation also indicates that a further adjustment of NLDAS-2 to form the best precipitation estimation should consider spatial–temporal distribution of bias. The satellite-only products have lower performance (peak and timing) than other products, while simple bias adjustment can intermediately improve the quality of simulated streamflow. The TMPA research product (TMPA-RP; research-quality data) can generate results approaching those of the ground-based products with only monthly gauge-based adjustment to the TMPA real-time product (TMPA-RT; near-real-time data). It is further noted that the streamflow bias is strongly correlated to precipitation bias at various time scales, though other factors may play a role as well, especially on the daily time scale.

© 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: Huan Wu, huanwu@umd.edu

This article is included in the IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission Special Collection.

Save