• AghaKouchak, A., L. Cheng, O. Mazdiyasni, and A. Farahmand, 2014: Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought. Geophys. Res. Lett., 41, 88478852, doi:10.1002/2014GL062308.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bell, J. E., and Coauthors, 2013: U.S. Climate Reference Network soil moisture and temperature observations. J. Hydrometeor., 14, 977988, doi:10.1175/JHM-D-12-0146.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bell, J. E., R. D. Leeper, M. A. Palecki, E. Coopersmith, T. Wilson, R. Bilotta, and S. Embler, 2015: Evaluation of the 2012 drought with a newly established National Soil Monitoring Network. Vadose Zone J., 14, doi:10.2136/vzj2015.02.0023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, S. K., and Coauthors, 2016: Assessment of the SMAP Passive Soil Moisture Product. IEEE Trans. Geosci. Remote Sens., 54, 49945007, doi:10.1109/TGRS.2016.2561938.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface−hydrology model with the Penn State−NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569585, doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diamond, H. J., and Coauthors, 2013: U.S. Climate Reference Network after one decade of operations: Status and assessment. Bull. Amer. Meteor. Soc., 94, 485498, doi:10.1175/BAMS-D-12-00170.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dominguez, F., and P. Kumar, 2008: Precipitation recycling variability and ecoclimatological stability—A study using NARR data. Part I: Central U.S. Plains ecoregion. J. Climate, 21, 51655186, doi:10.1175/2008JCLI1756.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dorigo, W., and Coauthors, 2011: The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci., 15, 16751698, doi:10.5194/hess-15-1675-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, Y., H. van den Dool, and W. Wu, 2011: Verification and intercomparison of multimodel simulated land surface hydrological datasets over the United States. J. Hydrometeor., 12, 531555, doi:10.1175/2011JHM1317.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T. W., E. Harris, and S. M. Quiring, 2014: Estimating root zone soil moisture using near-surface observations from SMOS. Hydrol. Earth Syst. Sci., 18, 139154, doi:10.5194/hess-18-139-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T. W., D. B. McRoberts, S. M. Quiring, and R. E. Hall, 2015: On the utility of in situ soil moisture observations for flash drought early warning in Oklahoma, USA. Geophys. Res. Lett., 42, 97909798, doi:10.1002/2015GL066600.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gutowski, W. J., S. G. Decker, R. A. Donavon, Z. Pan, R. W. Arritt, and E. S. Takle, 2003: Temporal−spatial scales of observed and simulated precipitation in central U.S. climate. J. Climate, 16, 38413847, doi:10.1175/1520-0442(2003)016<3841:TSOOAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoerling, M., J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, S. Schubert, and R. Seager, 2014: Causes and predictability of the 2012 Great Plains drought. Bull. Amer. Meteor. Soc., 95, 269282, doi:10.1175/BAMS-D-13-00055.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jackson, T. J., and Coauthors, 2012: Validation of Soil Moisture and Ocean Salinity (SMOS) soil moisture over watershed networks in the U.S. IEEE Trans. Geosci. Remote Sens., 50, 15301543, doi:10.1109/TGRS.2011.2168533.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, R. G., J. M. Murphy, and M. Noguer, 1995: Simulation of climate change over Europe using a nested regional-climate model. I: Assessment of control climate, including sensitivity to location of lateral boundaries. Quart. J. Roy. Meteor. Soc., 121, 14131449, doi:10.1002/qj.49712152610.

    • Search Google Scholar
    • Export Citation
  • Karl, T. R., and W. J. Koss, 1984: Regional and National Monthly, Seasonal, and Annual Temperature Weighted by Area, 1895–1983. Historical Climatography Series 4-3, NOAA/NESDIS/NCDC, 38 pp.

  • Karl, T. R., and Coauthors 2012: U.S. temperature and drought: Recent anomalies and trends. Eos, Trans. Amer. Geophys. Union, 93, 473474, doi:10.1029/2012EO470001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., Z. Guo, R. Yang, P. Dirmeyer, K. Mitchell, and M. Puma, 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 43224335, doi:10.1175/2009JCLI2832.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Legates, D. R., and G. J. McCabe, 1999: Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res., 35, 233241, doi:10.1029/1998WR900018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Legates, D. R., R. Mahmood, D. F. Levia, T. L. DeLiberty, S. M. Quiring, C. Houser, and F. E. Nelson, 2011: Soil moisture: A central and unifying theme in physical geography. Prog. Phys. Geogr., 35, 6586, doi:10.1177/0309133310386514.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPherson, A. R., 2007: A review of vegetation–atmosphere interactions and their influences on mesoscale phenomena. Prog. Phys. Geogr., 31, 261285, doi:10.1177/0309133307079055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360, doi:10.1175/BAMS-87-3-343.

  • Mitchell, K., 2005: The community Noah land-surface model (LSM) user’s guide, version 2.7.1. NOAA/NCEP Doc., 26 pp. [Available online at http://www.ral.ucar.edu/research/land/technology/lsm/noah/Noah_LSM_USERGUIDE_2.7.1.pdf.]

  • Nigam, S., and A. Ruiz-Barradas, 2006: Seasonal hydroclimate variability over North America in global and regional reanalysis and AMIP simulations: Varied representation. J. Climate, 19, 815837, doi:10.1175/JCLI3635.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ochsner, T. E., M. H. Cosh, R. H. Cuenca, W. A. Dorigo, C. S. Draper, Y. Hagimoto, and M. Zreda, 2013: State of the art in large-scale soil moisture monitoring. Soil. Sci. Soc. Amer. J., 77, 18881919, doi:10.2136/sssaj2013.03.0093.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quiring, S. M., J. M. Lucido, L. A. Winslow, T. W. Ford, P. B. Baruah, J. P. Verdin, R. S. Pulwarty, and M. L. Strobel, 2015: Development of a coordinated National Soil Moisture Network: A pilot study. 2015 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, Abstract IN41B-1704.

  • Quiring, S. M., T. W. Ford, J. K. Wang, A. Khong, E. Harris, T. Lindgren, D. W. Goldberg, and Z. Li, 2016: The North American Soil Moisture Database: Development and applications. Bull. Amer. Meteor. Soc., 97, 14411459, doi:10.1175/BAMS-D-13-00263.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robock, A., K. Vinnikov, G. Srinivasan, J. Entin, S. Hollinger, N. Speranskaya, S. Liu, and A. Namkhai, 2000: The Global Soil Moisture Data Bank. Bull. Amer. Meteor. Soc., 81, 12811299, doi:10.1175/1520-0477(2000)081<1281:TGSMDB>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., and Coauthors, 2010: Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Sci. Rev., 99, 125161, doi:10.1016/j.earscirev.2010.02.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seyfried, M. S., L. E. Grant, E. Du, and K. Humes, 2005: Dielectric loss and calibration of the hydra probe soil water sensor. Vadose Zone J., 4, 10701079, doi:10.2136/vzj2004.0148.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheffield, J., G. Goteti, F. Wen, and E. F. Wood, 2004: A simulated soil moisture based drought analysis for the United States. J. Geophys. Res., 109, D24108, doi:10.1029/2004JD005182.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, A., N. Lott, T. Houston, K. Shein, J. Crouch, and J. Enloe, 2016: U.S. billion-dollar weather & climate disasters 1980–2016. NOAA National Centers for Environmental Information, accessed 15 November 2016. [Available online at https://www.ncdc.noaa.gov/billions/events.pdf.]

  • Steinemann, A., 2003: Drought indicators and triggers: A stochastic approach to evaluation. J. Amer. Water Resour. Assoc., 39, 12171233, doi:10.1111/j.1752-1688.2003.tb03704.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and C. J. Guillemot, 1996: Physical processes involved in the 1988 drought and 1993 floods in North America. J. Climate, 9, 12881298, doi:10.1175/1520-0442(1996)009<1288:PPIITD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, G. van der Schrier, P. D. Jones, J. Barichivich, K. R. Briffa, and J. Sheffield, 2013: Global warming and changes in drought. Nat. Climate Change, 4, 1722, doi:10.1038/nclimate2067.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Willmott, C. J., 1981: On the validation of models. Phys. Geogr., 2, 184194.

  • Xia, Y., M. Ek, Y. Wu, T. Ford, and S. Quiring, 2015a: Comparison of NLDAS-2 simulated and NASMD observed daily soil moisture. Part I: Comparison and analysis. J. Hydrometeor., 16, 19621980, doi:10.1175/JHM-D-14-0096.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xia, Y., T. W. Ford, Y. Wu, S. M. Quiring, and M. B. Ek, 2015b: Automated quality control of in situ soil moisture from the North American Soil Moisture Database using NLDAS-2 products. J. Appl. Meteor. Climatol., 54, 12671282, doi:10.1175/JAMC-D-14-0275.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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An Evaluation of the North American Regional Reanalysis Simulated Soil Moisture Conditions during the 2011–13 Drought Period

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  • 1 Cooperative Institute for Climate and Satellites, North Carolina State University, Raleigh, and NOAA/National Centers for Environmental Information, Asheville, North Carolina
  • | 2 NOAA/National Centers for Environmental Information, Asheville, North Carolina, and Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, Ohio
  • | 3 NOAA/National Centers for Environmental Information, Asheville, North Carolina
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Abstract

Accurate and timely information on soil moisture conditions is an important component to effectively prepare for the damaging aspects of hydrological extremes. The combination of sparsely dense in situ networks and shallow observation depths of remotely sensed soil moisture conditions often force local and regional decision-makers to rely on numerical methods when assessing the current soil state. In this study, soil moisture from a commonly used, high-resolution reanalysis dataset is compared to observations from the U.S. Climate Reference Network (USCRN). The purpose of this study is to evaluate how well the North American Regional Reanalysis (NARR) captured the evolution, intensity, and spatial extent of the 2012 drought using both raw volumetric values and standardized anomalies of soil moisture. Comparisons revealed that despite a dry precipitation bias of 22% nationally, NARR had predominantly wetter 5-cm volumetric soil conditions over the growing season (April–September) than observed at USCRN sites across the contiguous United States, with differences more pronounced in drier regions. These biases were partially attributed to differences between the dominant soil characteristics assigned to the modeled grid cells and localized soil characteristics at the USCRN stations. However, NARR was able to successfully capture many aspects of the 2012 drought, including the timing, intensity, and spatial extent when using standardized soil moisture anomalies. Standardizing soil moisture conditions reduced the magnitude of systematic biases between NARR and USCRN in many regions and provided a more robust basis for utilizing modeled soil conditions in assessments of hydrological extremes.

© 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: Ronald D. Leeper, ronald.leeper@noaa.gov

Abstract

Accurate and timely information on soil moisture conditions is an important component to effectively prepare for the damaging aspects of hydrological extremes. The combination of sparsely dense in situ networks and shallow observation depths of remotely sensed soil moisture conditions often force local and regional decision-makers to rely on numerical methods when assessing the current soil state. In this study, soil moisture from a commonly used, high-resolution reanalysis dataset is compared to observations from the U.S. Climate Reference Network (USCRN). The purpose of this study is to evaluate how well the North American Regional Reanalysis (NARR) captured the evolution, intensity, and spatial extent of the 2012 drought using both raw volumetric values and standardized anomalies of soil moisture. Comparisons revealed that despite a dry precipitation bias of 22% nationally, NARR had predominantly wetter 5-cm volumetric soil conditions over the growing season (April–September) than observed at USCRN sites across the contiguous United States, with differences more pronounced in drier regions. These biases were partially attributed to differences between the dominant soil characteristics assigned to the modeled grid cells and localized soil characteristics at the USCRN stations. However, NARR was able to successfully capture many aspects of the 2012 drought, including the timing, intensity, and spatial extent when using standardized soil moisture anomalies. Standardizing soil moisture conditions reduced the magnitude of systematic biases between NARR and USCRN in many regions and provided a more robust basis for utilizing modeled soil conditions in assessments of hydrological extremes.

© 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: Ronald D. Leeper, ronald.leeper@noaa.gov
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