• Alley, W. M., 1984: The Palmer drought severity index: Limitations and assumptions. J. Climate Appl. Meteor., 23, 11001109, https://doi.org/10.1175/1520-0450(1984)023<1100:TPDSIL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Applequist, S., A. Arguez, I. Dure, M. F. Squires, R. S. Vose, and X. Yin, 2012: 1981–2010 U.S. hourly normal. Bull. Amer. Meteor. Soc., 93, 16371640, https://doi.org/10.1175/BAMS-D-11-00173.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bolten, J. D., W. T. Crow, X. Zhan, T. J. Jackson, and C. A. Reynolds, 2010: Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 3, 5766, https://doi.org/10.1109/JSTARS.2009.2037163.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brocca, L., R. Morbidelli, F. Melone, and T. Moramarco, 2007: Soil moisture spatial variability in experimental areas of central Italy. J. Hydrol., 333, 356373, https://doi.org/10.1016/j.jhydrol.2006.09.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coopersmith, E. J., M. H. Cosh, J. E. Bell, V. Kelly, M. Hall, M. A. Palecki, and M. Temimi, 2016: Deploying temporary networks for upscaling of sparse network stations. Int. J. Appl. Earth Obs. Geoinf., 52, 433444, https://doi.org/10.1016/j.jag.2016.07.013.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., 2011: The terrestrial segment of soil moisture–climate coupling. Geophys. Res. Lett., 38, L16702, https://doi.org/10.1029/2011GL048268.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Entin, J. K., A. Robock, K. Y. Vinnikov, S. E. Hollinger, S. Liu, and A. Namkhai, 2000: Temporal and spatial scales of observed soil moisture variations in the extratropics. J. Geophys. Res., 105, 11 86511 877, https://doi.org/10.1029/2000JD900051.

    • 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, https://doi.org/10.1002/2015GL066600.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T. W., Q. Wang, and S. M. Quiring, 2016: The observation record length necessary to generate robust soil moisture percentiles. J. Appl. Meteor. Climatol., 55, 21312149, https://doi.org/10.1175/JAMC-D-16-0143.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heim, R. R., 2002: A review of twentieth-century drought indices used in the United States. Bull. Amer. Meteor. Soc., 83, 11491166, https://doi.org/10.1175/1520-0477-83.8.1149.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krueger, E. S., T. E. Ochsner, and S. M. Quiring, 2019: Development and evaluation of soil moisture-based indices for agricultural drought monitoring. Agron. J., 111, 115, https://doi.org/10.2134/agronj2018.09.0558.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leeper, R. D., J. E. Bell, C. Vines, and M. Palecki, 2017: An evaluation of the North American regional reanalysis simulated soil moisture conditions during the 2011-13 drought period. J. Hydrometeor., 18, 515527, https://doi.org/10.1175/JHM-D-16-0132.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leeper, R. D., J. E. Bell, and M. A. Palecki, 2019: A description and evaluation of U.S. Climate Reference Network standardized soil moisture dataset. J. Appl. Meteor. Climatol., 58, 14171428, https://doi.org/10.1175/JAMC-D-18-0269.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martínez-Fernández, J., A. González-Zamora, N. Sánchez, A. Gumuzzio, and C. M. Herrero-Jiménez, 2016: Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived soil water deficit index. Remote Sens. Environ., 177, 277286, https://doi.org/10.1016/j.rse.2016.02.064.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mkhabela, M., P. Bullock, M. Gervais, G. Finlay, and H. Sapirstein, 2010: Assessing indicators of agricultural drought impacts on spring wheat yield and quality on the Canadian prairies. Agric. For. Meteor., 150, 399410, https://doi.org/10.1016/j.agrformet.2010.01.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mo, K. C., 2011: Drought onset and recovery over the United States. J. Geophys. Res., 116, D20106, https://doi.org/10.1029/2011JD016168.

    • Search Google Scholar
    • Export Citation
  • Narasimhan, B., and R. Srinivasan, 2005: Development and evaluation of soil moisture deficit index (SMDI) and evapotranspiration deficit index (ETDI) for agricultural drought monitoring. Agric. For. Meteor., 133, 69–88, https://doi.org/10.1016/j.agrformet.2005.07.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, W. C., 1965: Meteorological drought. U.S. Weather Bureau Research Paper 45, 65 pp., https://www.ncdc.noaa.gov/temp-and-precip/drought/docs/palmer.pdf.

  • Palmer, W. C., 1968: Keeping track of crop moisture conditions, nationwide: The new crop moisture index. Weatherwise, 21, 156161, https://doi.org/10.1080/00431672.1968.9932814.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Panu, U. S., and T. C. Sharma, 2002: Challenges in drought research: Some perspectives and future directions. Hydrol. Sci. J., 47 (Suppl. 1), S19S30, https://doi.org/10.1080/02626660209493019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Svoboda, M., L. Doug, H. Mike, H. Richard, and K. Gleason, 2002: The Drought Monitor. Bull. Amer. Meteor. Soc., 83, 11811190, https://doi.org/10.1175/1520-0477-83.8.1181.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torres, G. M., R. P. Lollato, and T. E. Ochsner, 2013: Comparison of drought probability assessments based on atmospheric water deficit and soil water deficit. Agron. J., 105, 428436, https://doi.org/10.2134/agronj2012.0295.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vicente-Serrano, S. M., S. Beguería, and J. I. López-Moreno, 2010: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Climate, 23, 16961718, https://doi.org/10.1175/2009JCLI2909.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viterbo, F., and et al. , 2020: A multiscale, hydrometeorological forecast evaluation of national water model forecasts of the May 2018 Ellicott City, Maryland, Flood. J. Hydrometeor., 21, 475499, https://doi.org/10.1175/JHM-D-19-0125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wells, N., S. Goddard, and M. J. Hayes, 2004: A self-calibrating Palmer drought severity index. J. Climate, 17, 23352351, https://doi.org/10.1175/1520-0442(2004)017<2335:ASPDSI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xia, Y., J. Sheffield, M. B. Ek, J. Dong, N. Chaney, H. Wei, J. Meng, and E. F. Wood, 2014: Evaluation of multi-model simulated soil moisture in NLDAS-2. J. Hydrol., 512, 107125, https://doi.org/10.1016/j.jhydrol.2014.02.027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zargar, A., S. Rehan, N. Bahman, and I. K. Faisal, 2011: A review of drought indices. Environ. Rev., 19, 333349, https://doi.org/10.1139/a11-013.

All Time Past Year Past 30 Days
Abstract Views 248 248 55
Full Text Views 60 60 15
PDF Downloads 84 84 22

Exploring the Use of Standardized Soil Moisture as a Drought Indicator

View More View Less
  • 1 a Cooperative Institute for Climate and Satellites–North Carolina, North Carolina State University, Asheville, North Carolina
  • | 2 b NOAA/National Centers for Environmental Information, Asheville, North Carolina
  • | 3 c Iowa State University, Ames, Iowa
  • | 4 d NOAA/Air Resources Laboratory, College Park, Maryland
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

Agricultural drought has traditionally been monitored using indices that are based on above-ground measures of temperature and precipitation that have lengthy historical records. However, the period-of-record length for soil moisture networks is becoming sufficient enough to standardize and evaluate soil moisture anomalies and percentiles that are spatially and temporally independent of local soil type, topography, and climatology. To explore these standardized measures in the context of drought, the U.S. Climate Reference Network hourly standardized soil moisture anomalies and percentiles were evaluated against changes in the U.S. Drought Monitor (USDM) status, with a focus on onset, worsening, and improving drought conditions. The purpose of this study was to explore time scales (i.e., 1–6 weeks) and soil moisture at individual (i.e., 5, 10, 20, 50, and 100 cm) and aggregated layer (i.e., top and column) depths to determine those that were more closely align with evolving drought conditions. Results indicated that the upper-level depths (5, 10, and 20 cm, and top layer aggregate) and shorter averaging periods were more responsive to changes in USDM drought status. This was particularly evident during the initial and latter stages of drought when USDM status changes were thought to be more aligned with soil moisture conditions. This result indicates that standardized measures of soil moisture can be useful in drought monitoring and forecasting applications during these critical stages of drought formation and amelioration.

Significance Statement

Drought is normally monitored by making inferences from temperature and precipitation observations. In this study, we explored whether soil moisture data would improve our ability to monitor evolving drought conditions. Results showed that soil moisture observations were drier than usual prior to U.S. Drought Monitor onset for nearly 80% of events and worsening drought weeks. For improving weeks, soil moisture observations were only slightly drier than usual or near normal. This was more pronounced in the initial and final few weeks of drought. This suggests that applications of soil moisture measurements to monitor and anticipate evolving drought conditions are best focused on the critical stages of drought formation and termination.

© 2021 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: Ronald D. Leeper, ronnieleeper@cicsnc.org

Abstract

Agricultural drought has traditionally been monitored using indices that are based on above-ground measures of temperature and precipitation that have lengthy historical records. However, the period-of-record length for soil moisture networks is becoming sufficient enough to standardize and evaluate soil moisture anomalies and percentiles that are spatially and temporally independent of local soil type, topography, and climatology. To explore these standardized measures in the context of drought, the U.S. Climate Reference Network hourly standardized soil moisture anomalies and percentiles were evaluated against changes in the U.S. Drought Monitor (USDM) status, with a focus on onset, worsening, and improving drought conditions. The purpose of this study was to explore time scales (i.e., 1–6 weeks) and soil moisture at individual (i.e., 5, 10, 20, 50, and 100 cm) and aggregated layer (i.e., top and column) depths to determine those that were more closely align with evolving drought conditions. Results indicated that the upper-level depths (5, 10, and 20 cm, and top layer aggregate) and shorter averaging periods were more responsive to changes in USDM drought status. This was particularly evident during the initial and latter stages of drought when USDM status changes were thought to be more aligned with soil moisture conditions. This result indicates that standardized measures of soil moisture can be useful in drought monitoring and forecasting applications during these critical stages of drought formation and amelioration.

Significance Statement

Drought is normally monitored by making inferences from temperature and precipitation observations. In this study, we explored whether soil moisture data would improve our ability to monitor evolving drought conditions. Results showed that soil moisture observations were drier than usual prior to U.S. Drought Monitor onset for nearly 80% of events and worsening drought weeks. For improving weeks, soil moisture observations were only slightly drier than usual or near normal. This was more pronounced in the initial and final few weeks of drought. This suggests that applications of soil moisture measurements to monitor and anticipate evolving drought conditions are best focused on the critical stages of drought formation and termination.

© 2021 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: Ronald D. Leeper, ronnieleeper@cicsnc.org
Save