• Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, 300 pp., www.fao.org/docrep/X0490E/X0490E00.htm.

  • Atkinson, S. E., R. A. Woods, and M. Sivapalan, 2002: Climate and landscape controls on water balance model complexity over changing timescales. Water Resour. Res., 38, 1314, https://doi.org/10.1029/2002WR001487.

    • Search Google Scholar
    • Export Citation
  • Auerbach, D. A., Z. M. Easton, M. T. Walter, A. S. Flecker, and D. R. Fuka, 2016: Evaluating weather observations and the climate forecast system reanalysis as inputs for hydrologic modelling in the tropics. Hydrol. Processes, 30, 34663477, https://doi.org/10.1002/hyp.10860.

    • Search Google Scholar
    • Export Citation
  • Ayzel, G., 2016a: LHMP: First major release. Zenodo, https://doi.org/10.5281/zenodo.59680.

  • Ayzel, G., 2016b: LHMP: Lumped hydrological modelling playground. Zenodo, https://doi.org/10.5281/zenodo.59501.

  • Beck, H. E., and Coauthors, 2017: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modelling. Hydrol. Earth Syst. Sci., 21, 62016217, https://doi.org/10.5194/hess-21-6201-2017.

    • Search Google Scholar
    • Export Citation
  • Beck, H. E., and Coauthors, 2019: Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS. Hydrol. Earth Syst. Sci., 23, 207224, https://doi.org/10.5194/hess-23-207-2019.

    • Search Google Scholar
    • Export Citation
  • Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese, 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present. Earth Syst. Sci. Data, 5, 7199, https://doi.org/10.5194/essd-5-71-2013.

    • Search Google Scholar
    • Export Citation
  • Behnke, R., S. Vavrus, A. Allstadt, T. Albright, W. E. Thogmartin, and V. C. Radeloff, 2016: Evaluation of downscaled, gridded climate data for the conterminous United States. Ecol. Appl., 26, 13381351, https://doi.org/10.1002/15-1061.

    • Search Google Scholar
    • Export Citation
  • Bergström, S., 1992: The HBV model – Its structure and applications. SMHI Rep. RH4, Swedish Meteorological and Hydrological Institute, Norrköpping, Sweden, 44 pp., https://www.smhi.se/polopoly_fs/1.83592!/Menu/general/extGroup/attachmentColHold/mainCol1/file/RH_4.pdf.

  • Berthet, L., V. Andréassian, C. Perrin, and C. Loumagne, 2010: How significant are quadratic criteria? Part II On the relative contribution of large flood events to the value of a quadratic criterion. Hydrol. Sci. J., 55, 10631073, https://doi.org/10.1080/02626667.2010.505891.

    • Search Google Scholar
    • Export Citation
  • Brettle, M. J., and J. F. P. Galvin, 2003: Back to basics: Radiosondes: Part I – The instrument. Weather, 58, 336341, https://doi.org/10.1256/wea.126.02A.

    • Search Google Scholar
    • Export Citation
  • Burt, T. P., 1994: Long-term study of the natural environment - Perceptive science or mindless monitoring? Prog. Phys. Geogr., 18, 475496, https://doi.org/10.1177/030913339401800401.

    • Search Google Scholar
    • Export Citation
  • Choi, W., S. J. Kim, P. F. Rasmussen, and A. R. Moore, 2009: Use of the North American regional reanalysis for hydrological modelling in Manitoba. Can. Water Resour. J., 34, 1736, https://doi.org/10.4296/cwrj3401017.

    • Search Google Scholar
    • Export Citation
  • Ciabatta, L., A. C. Marra, G. Panegrossi, D. Casella, P. Sanò, S. Dietrich, C. Massari, and L. Brocca, 2017: Daily precipitation estimation through different microwave sensors: Verification study over Italy. J. Hydrol., 545, 436450, https://doi.org/10.1016/j.jhydrol.2016.12.057.

    • Search Google Scholar
    • Export Citation
  • Compo, G. P., J. S. Whitaker, and P. D. Sardeshmukh, 2006: Feasibility of a 100-year reanalysis using only surface pressure data. Bull. Amer. Meteor. Soc., 87, 175190, https://doi.org/10.1175/BAMS-87-2-175.

    • Search Google Scholar
    • Export Citation
  • Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis project. Quart. J. Roy. Meteor. Soc., 137, 128, https://doi.org/10.1002/qj.776.

    • Search Google Scholar
    • Export Citation
  • Cram, T. A., and Coauthors, 2015: The International Surface Pressure Databank version 2. Geosci. Data J., 2, 3146, https://doi.org/10.1002/gdj3.25.

    • Search Google Scholar
    • Export Citation
  • Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140158, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • de Boisséson E., and M. Balmaseda, 2016: An ensemble of 20th century ocean reanalyses for providing ocean initial conditions for CERA-20C coupled streams. ERA Rep. Series 24, 35 pp., https://www.ecmwf.int/node/16456.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • Duan, Q., S. Sorooshian, and V. K. Gupta, 1994: Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol., 158, 265284, https://doi.org/10.1016/0022-1694(94)90057-4.

    • Search Google Scholar
    • Export Citation
  • Dupigny-Giroux L.-A., T. F. Ross, J. D. Elms, R. Truesdell, and S. R. Doty, 2007: NOAA's Climate Database Modernization Program: Rescuing, archiving, and digitizing history. Bull. Amer. Meteor. Soc., 88, 10151017, https://doi.org/10.1175/1520-0477-88.7.1007.

    • Search Google Scholar
    • Export Citation
  • Essou, G. R., F. Sabarly, P. Lucas-Picher, F. Brissette, and A. Poulin, 2016: Can precipitation and temperature from meteorological reanalyses be used for hydrological modeling? J. Hydrometeor., 17, 19291950, https://doi.org/10.1175/JHM-D-15-0138.1.

    • Search Google Scholar
    • Export Citation
  • Feigenwinter, I., S. Kotlarski, A. Casanueva, A. M. Fischer, C. Schwierz, and M. A. Liniger, 2018: Exploring quantile mapping as a tool to produce user-tailored climate scenarios for Switzerland. Tech. Rep. MeteoSwiss 270, 44 pp., https://www.meteoschweiz.admin.ch/content/dam/meteoswiss/en/service-und-publikationen/publikationen/doc/MeteoSchweiz_Fachbericht_270_final.pdf.

  • Gudmundsson, L., 2016: qmap: Statistical transformations for post-processing climate model output, version 1.0-4. R package, https://cran.r-project.org/web/packages/qmap/qmap.pdf.

  • Gudmundsson, L., J. B. Bremnes, J. E. Haugen, and T. Engen-Skaugen, 2012: Technical note: Downscaling RCM precipitation to the station scale using statistical transformations - A comparison of methods. Hydrol. Earth Syst. Sci., 16, 33833390, https://doi.org/10.5194/hess-16-3383-2012.

    • Search Google Scholar
    • Export Citation
  • Hargreaves, G. H., and Z. A. Samani, 1982: Estimating potential evapotranspiration. J. Irrig. Drain. Div., 108, 225230.

  • Hargreaves, G. H., and R. G. Allen, 2003: History and evaluation of Hargreaves evapotranspiration equation. J. Irrig. Drain. Eng., 129, 5363, https://doi.org/10.1061/(ASCE)0733-9437(2003)129:1(53).

    • Search Google Scholar
    • Export Citation
  • Harris, I., P. Jones, T. Osborn, and D. Lister, 2014: Updated high-resolution grids of monthly climatic observations – The CRU TS3.10 dataset. Int. J. Climatol., 34, 623642, https://doi.org/10.1002/joc.3711.

    • Search Google Scholar
    • Export Citation
  • Henn, B., A. J. Newman, B. Livneh, C. Daly, and J. D. Lundquist, 2018: An assessment of differences in gridded precipitation datasets in complex terrain. J. Hydrol., 556, 12051219, https://doi.org/10.1016/j.jhydrol.2017.03.008.

    • Search Google Scholar
    • Export Citation
  • Klemeš, V., 1986: Operational testing of hydrological simulation models. Hydrol. Sci. J., 31, 1324, https://doi.org/10.1080/02626668609491024.

    • Search Google Scholar
    • Export Citation
  • Kling, H., M. Fuchs, and M. Paulin, 2012: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. J. Hydrol., 424-425, 264277, https://doi.org/10.1016/j.jhydrol.2012.01.011.

    • Search Google Scholar
    • Export Citation
  • Krogh, S. A., J. W. Pomeroy, and J. McPhee, 2015: Physically based mountain hydrological modeling using reanalysis data in Patagonia. J. Hydrometeor., 16, 172193, https://doi.org/10.1175/JHM-D-13-0178.1.

    • Search Google Scholar
    • Export Citation
  • Laiti, L., S. Mallucci, S. Piccolroaz, A. Bellin, D. Zardi, A. Fiori, G. Nikulin, and B. Majone, 2018: Testing the hydrological coherence of high-resolution gridded precipitation and temperature data sets. Water Resour. Res., 54, 19992016, https://doi.org/10.1002/2017WR021633.

    • Search Google Scholar
    • Export Citation
  • Laloyaux, P., and D. Dee, 2015: CERA: A coupled data assimilation system for climate reanalysis. ECMWF Newsletter, No. 144, ECMWF, Reading, United Kingdom, 15–20, https://doi.org/10.21957/0dx9o7bg.

  • Laloyaux, P., M. Balmaseda, D. Dee, K. Mogensen, and P. Janssen, 2016: A coupled data assimilation system for climate reanalysis. Quart. J. Roy. Meteor. Soc., 142, 6578, https://doi.org/10.1002/qj.2629.

    • Search Google Scholar
    • Export Citation
  • Laloyaux, P., F. de Boisséson, and P. Dahlgren, 2017: CERA-20C: An earth system approach to climate reanalysis. ECMWF Newsletter, No. 150, ECMWF, Reading, United Kingdom, 25–30, https://doi.org/10.21957/ffs36birj2.

  • Laloyaux, P., and Coauthors, 2018: CERA-20C: A coupled reanalysis of the twentieth century. J. Adv. Model. Earth Syst., 10, 11721195, https://doi.org/10.1029/2018MS001273.

    • Search Google Scholar
    • Export Citation
  • Lauri, H., T. A. Räsänen, and M. Kummu, 2014: Using reanalysis and remotely sensed temperature and precipitation data for hydrological modeling in monsoon climate: Mekong River case study. J. Hydrometeor., 15, 15321545, https://doi.org/10.1175/JHM-D-13-084.1.

    • Search Google Scholar
    • Export Citation
  • Li, H., S. Beldring, and C.-Y. Xu, 2015: Stability of model performance and parameter values on two catchments facing changes in climatic conditions. Hydrol. Sci. J., 60, 13171330, https://doi.org/10.1080/02626667.2014.978333.

    • Search Google Scholar
    • Export Citation
  • Lindström, G., B. Johansson, M. Persson, M. Gardelin, and S. Bergström, 1997: Development and test of the distributed HBV-96 hydrological model. J. Hydrol., 201, 272288, https://doi.org/10.1016/S0022-1694(97)00041-3.

    • Search Google Scholar
    • Export Citation
  • Livneh, B., E. A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K. M. Andreadis, E. P. Maurer, and D. P. Lettenmaier, 2013: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions. J. Climate, 26, 93849392, https://doi.org/10.1175/JCLI-D-12-00508.1.

    • Search Google Scholar
    • Export Citation
  • Livneh, B., T. J. Bohn, D. S. Pierce, F. Munoz-Ariola, B. Nijssen, R. Vose, D. Cayan, and L. D. Brekke, 2015a: A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and southern Canada 1950–2013. Sci. Data, 2, 150042, https://doi.org/10.1038/sdata.2015.42.

    • Search Google Scholar
    • Export Citation
  • Livneh, B., R. Kumar, and L. Samaniego, 2015b: Influence of soil textural properties on hydrologic fluxes in the Mississippi river basin. Hydrol. Processes, 29, 46384655, https://doi.org/10.1002/hyp.10601.

    • Search Google Scholar
    • Export Citation
  • Maraun, D., 2016: Bias correcting climate change simulations - A critical review. Curr. Climate Change Rep., 2, 211220, https://doi.org/10.1007/s40641-016-0050-x.

    • Search Google Scholar
    • Export Citation
  • Massmann, C., 2020: Identification of factors influencing hydrologic model performance using a top-down approach in a large number of U.S. catchments. Hydrol. Processes, 34, 420, https://doi.org/10.1002/hyp.13566.

    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., A. W. Wood, J. C. Adam, B. Nijssen, and D. P. Lettenmaier, 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J. Climate, 15, 32373251, https://doi.org/10.1175/1520-0442(2002)015<3237:ALTHBD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mendoza, P. A., and Coauthors, 2015: Effects of hydrologic model choice and calibration on the portrayal of climate change impacts. J. Hydrometeor., 16, 762780, https://doi.org/10.1175/JHM-D-14-0104.1.

    • Search Google Scholar
    • Export Citation
  • Menne, M. J., C. N. Williams, B. E. Gleason, J. J. Rennie, and J. H. Lawrimore, 2018: The Global Historical Climatology Network monthly temperature dataset, version 4. J. Climate, 31, 98359854, https://doi.org/10.1175/JCLI-D-18-0094.1.

    • Search Google Scholar
    • Export Citation
  • Merz, R., J. Parajka, and G. Blöschl, 2011: Time stability of catchment model parameters: Implications for climate impact analyses. Water Resour. Res., 47, W02531, https://doi.org/10.1029/2010WR009505.

    • Search Google Scholar
    • Export Citation
  • Newman, A. J., and Coauthors, 2015: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: Data set characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209223, https://doi.org/10.5194/hess-19-209-2015.

    • Search Google Scholar
    • Export Citation
  • Northrop, P. J., and R. E. Chandler, 2014: Quantifying sources of uncertainty in projections of future climate. J. Climate, 27, 87938808, https://doi.org/10.1175/JCLI-D-14-00265.1.

    • Search Google Scholar
    • Export Citation
  • Panofsky, H. A., and G. W. Brier, 1968: Some Applications of Statistics to Meteorology. Earth and Mineral Sciences Continuing Education, College of Earth and Mineral Sciences, 224 pp.

  • Poli, P., R. Saunders, and D. Santek, 2015: Rescuing satellite data for climate reanalysis. ECMWF Newsletter, No. 144, ECMWF, Reading, United Kingdom, 8–9, https://www.ecmwf.int/node/14588.

  • Poli, P., and Coauthors, 2017: Recent advances in satellite data rescue. Bull. Amer. Meteor. Soc., 98, 14711484, https://doi.org/10.1175/BAMS-D-15-00194.1.

    • Search Google Scholar
    • Export Citation
  • Raimonet, M., L. Oudin, V. Thieu, M. Silvestre, R. Vautard, C. Rabouille, and P. Le Moigne, 2017: Evaluation of gridded meteorological datasets for hydrological modeling. J. Hydrometeor., 18, 30273041, https://doi.org/10.1175/JHM-D-17-0018.1.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and Coauthors, 2012: How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811829, https://doi.org/10.1175/BAMS-D-11-00052.1.

    • Search Google Scholar
    • Export Citation
  • Schaefli, B., and H. V. Gupta, 2007: Do Nash values have value? Hydrol. Processes, 21, 20752080, https://doi.org/10.1002/hyp.6825.

  • Seiller, G., and F. Anctil, 2016: How do potential evapotranspiration formulas influence hydrological projections? Hydrol. Sci. J., 61, 22492266, https://doi.org/10.1080/02626667.2015.1100302.

    • Search Google Scholar
    • Export Citation
  • Shepard, D. S., 1984: Computer mapping: The SYMAP interpolation algorithm. Spatial Statistics and Models, G. L. Gaile and C. J. Willmott, Eds., Theory and Decision Library, Vol. 40, Springer, 133–145, https://doi.org/10.1007/978-94-017-3048-8_7.

  • Sperna Weiland, F. C., C. Tisseuil, H. H. M. Dürr, M. Vrac, and L. P. H. van Beek, 2012: Selecting the optimal method to calculate daily global reference potential evaporation from CFSR reanalysis data for application in a hydrological model study. Hydrol. Earth Syst. Sci., 16, 9831000, https://doi.org/10.5194/hess-16-983-2012.

    • Search Google Scholar
    • Export Citation
  • Sun, Q., C. Miao, Q. Duan, H. Ashouri, S. Sorooshian, and K.-L. Hsu, 2018: A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Rev. Geophys., 56, 79107, https://doi.org/10.1002/2017RG000574.

    • Search Google Scholar
    • Export Citation
  • Thornton, P. E., and S. W. Running, 1999: An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agric. For. Meteor., 93, 211228, https://doi.org/10.1016/S0168-1923(98)00126-9.

    • Search Google Scholar
    • Export Citation
  • Thornton, P. E., S. W. Running, and M. A. White, 1997: Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol., 190, 214251, https://doi.org/10.1016/S0022-1694(96)03128-9.

    • Search Google Scholar
    • Export Citation
  • Thornton, P. E., H. Hasenauer, and M. A. White, 2000: Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: An application over complex terrain in Austria. Agric. For. Meteor., 104, 255271, https://doi.org/10.1016/S0168-1923(00)00170-2.

    • Search Google Scholar
    • Export Citation
  • Thornton, P. E., M. M. Thornton, B. W. Mayer, Y. Wei, R. Devarakonda, R. S. Vose, and R. B. Cook, 2018: Daymet: Daily surface weather data on a 1-km grid for North America, version 2. Oak Ridge National Laboratory Distributed Active Archive Center, accessed 25 October 2016, https://doi.org/10.3334/ORNLDAAC/1219.

  • Whitaker, J. S., G. P. Compo, X. Wei, and T. M. Hamill, 2004: Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev., 132, 11901200, https://doi.org/10.1175/1520-0493(2004)132<1190:RWRUED>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wilby, R. L., L. E. Hay, W. J. Gutowski, R. W. Arritt, E. S. Takle, Z. Pan, G. H. Leavesley, and M. P. Clark, 2000: Hydrological responses to dynamically and statistically downscaled climate model output. Geophys. Res. Lett., 27, 11991202, https://doi.org/10.1029/1999GL006078.

    • Search Google Scholar
    • Export Citation
  • Willmott, C. J., and K. Matsuura, 2001: Terrestrial precipitation: 1900–2017 gridded monthly time series. Dept. of Geography, University of Delaware, http://climate.geog.udel.edu/~climate/html_pages/Global2017/README.GlobalTsP2017.html.

All Time Past Year Past 30 Days
Abstract Views 7 7 7
Full Text Views 0 0 0
PDF Downloads 0 0 0

Evaluating the Suitability of Century-Long Gridded Meteorological Datasets for Hydrological Modeling

View More View Less
  • 1 Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria, and Department of Civil Engineering, University of Bristol, Bristol, United Kingdom
© Get Permissions
Restricted access

Abstract

Recent advances in climate reanalyses have led to the development of meteorological products providing information from the beginning of the last century or even before. As these data sources might be of interest to practitioners in the event of missing data from meteorological stations, it is important to assess their usefulness for different applications. The main objective of this study is to investigate the ability of two long-term reanalysis datasets (CERA-20C and 20CR) and one long-term interpolated dataset (Livneh) for supporting hydrological modeling. The precipitation and temperature data of the three datasets were first compared, downscaled, and then used as inputs to the conceptual hydrological model HBV in 168 basins in the United States. The findings suggest that the quality of all three datasets decreases the further we go back in time. Models calibrated at the beginning of the time series, where the data quality is worse, are only able to capture the general properties of the time series and thus do not show a decrease in performance as the period between calibration and validation becomes larger. The opposite is true for models calibrated at the end of the time series, which show a clear decrease in performance toward the beginning of the century. While the hydrological model driven with the interpolated datasets achieved the best performance, the results obtained with the reanalysis datasets were still informative (i.e., better than the long-term monthly mean), and they matched the performance of the interpolated dataset in a few catchments in the northwestern United States.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-19-0113.s1.

Denotes content that is immediately available upon publication as open access.

© 2020 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: Carolina Massmann, carolina.massmann@boku.ac.at

Abstract

Recent advances in climate reanalyses have led to the development of meteorological products providing information from the beginning of the last century or even before. As these data sources might be of interest to practitioners in the event of missing data from meteorological stations, it is important to assess their usefulness for different applications. The main objective of this study is to investigate the ability of two long-term reanalysis datasets (CERA-20C and 20CR) and one long-term interpolated dataset (Livneh) for supporting hydrological modeling. The precipitation and temperature data of the three datasets were first compared, downscaled, and then used as inputs to the conceptual hydrological model HBV in 168 basins in the United States. The findings suggest that the quality of all three datasets decreases the further we go back in time. Models calibrated at the beginning of the time series, where the data quality is worse, are only able to capture the general properties of the time series and thus do not show a decrease in performance as the period between calibration and validation becomes larger. The opposite is true for models calibrated at the end of the time series, which show a clear decrease in performance toward the beginning of the century. While the hydrological model driven with the interpolated datasets achieved the best performance, the results obtained with the reanalysis datasets were still informative (i.e., better than the long-term monthly mean), and they matched the performance of the interpolated dataset in a few catchments in the northwestern United States.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-19-0113.s1.

Denotes content that is immediately available upon publication as open access.

© 2020 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: Carolina Massmann, carolina.massmann@boku.ac.at

Supplementary Materials

    • Supplemental Materials (PDF 2.47 MB)
Save