Estimating Hydrological Regimes from Observational Soil Moisture, Evapotranspiration, and Air Temperature Data

R. D. Koster aGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by R. D. Koster in
Current site
Google Scholar
PubMed
Close
,
A. F. Feldman bBiospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland
cEarth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

Search for other papers by A. F. Feldman in
Current site
Google Scholar
PubMed
Close
,
T. R. H. Holmes dHydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by T. R. H. Holmes in
Current site
Google Scholar
PubMed
Close
,
M. C. Anderson eHydrology and Remote Sensing Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, Maryland

Search for other papers by M. C. Anderson in
Current site
Google Scholar
PubMed
Close
,
W. T. Crow eHydrology and Remote Sensing Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, Maryland

Search for other papers by W. T. Crow in
Current site
Google Scholar
PubMed
Close
, and
C. Hain fEarth Science Office, NASA Marshall Space Flight Center, Huntsville, Alabama

Search for other papers by C. Hain in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Evapotranspiration has long been understood to vary with soil moisture in drier regions and to be relatively insensitive to soil moisture in wetter regions. A number of recent studies have quantified this behavior with various model and observational datasets. However, given the disparate approaches and datasets used, uncertainty persists in how the underlying relationships vary in space and time. Here we complement the existing studies by analyzing two datasets as yet untapped for this purpose: a satellite-based evapotranspiration E product retrieved using geostationary thermal imagery and a meteorological-station-based dataset of daily 2-m air temperature (T2M) diurnal amplitudes. Both datasets are analyzed synchronously with soil moisture from the Soil Moisture Active Passive (SMAP) satellite. We thereby derive maps of evaporative regimes that vary in space and time as one might expect, that is, the water-limited regime grows eastward across the conterminous United States as spring moves into summer, only to shrink again going into winter. The relationship between the E and soil moisture data appears particularly tight, which is encouraging given that the E data (like the T2M data) were not constructed using any soil moisture information whatsoever. The general agreement between the two independent sets of results gives us confidence that the generated maps correctly represent, to first order, evaporative regime behavior in nature. The T2M results have the added benefit of highlighting the significant connection between soil moisture and overlying air temperature, a connection relevant to T2M predictability.

Significance Statement

When a soil is somewhat dry, an increase in soil moisture can lead to an increase in evapotranspiration E. In contrast, when a soil is wet, E is limited instead by the availability of energy. Determining where E is water limited, energy limited, or some combination of both is important because it tells us where accurate soil moisture initialization in a forecast system might contribute to more accurate forecasts of E and thus air temperature. Here we use a combination of independent datasets (satellite-derived estimates of soil moisture and E as well as air temperature measurements from weather stations) to provide new monthly maps of the water-limited, energy-limited, and combination regimes over the continental United States and across the world.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 22 April 2024 to update the link in the Data Availability Statement for the ALEXI E and CFSR-based downwelling solar radiation data.

Corresponding author: R. D. Koster, randal.d.koster@nasa.gov

Abstract

Evapotranspiration has long been understood to vary with soil moisture in drier regions and to be relatively insensitive to soil moisture in wetter regions. A number of recent studies have quantified this behavior with various model and observational datasets. However, given the disparate approaches and datasets used, uncertainty persists in how the underlying relationships vary in space and time. Here we complement the existing studies by analyzing two datasets as yet untapped for this purpose: a satellite-based evapotranspiration E product retrieved using geostationary thermal imagery and a meteorological-station-based dataset of daily 2-m air temperature (T2M) diurnal amplitudes. Both datasets are analyzed synchronously with soil moisture from the Soil Moisture Active Passive (SMAP) satellite. We thereby derive maps of evaporative regimes that vary in space and time as one might expect, that is, the water-limited regime grows eastward across the conterminous United States as spring moves into summer, only to shrink again going into winter. The relationship between the E and soil moisture data appears particularly tight, which is encouraging given that the E data (like the T2M data) were not constructed using any soil moisture information whatsoever. The general agreement between the two independent sets of results gives us confidence that the generated maps correctly represent, to first order, evaporative regime behavior in nature. The T2M results have the added benefit of highlighting the significant connection between soil moisture and overlying air temperature, a connection relevant to T2M predictability.

Significance Statement

When a soil is somewhat dry, an increase in soil moisture can lead to an increase in evapotranspiration E. In contrast, when a soil is wet, E is limited instead by the availability of energy. Determining where E is water limited, energy limited, or some combination of both is important because it tells us where accurate soil moisture initialization in a forecast system might contribute to more accurate forecasts of E and thus air temperature. Here we use a combination of independent datasets (satellite-derived estimates of soil moisture and E as well as air temperature measurements from weather stations) to provide new monthly maps of the water-limited, energy-limited, and combination regimes over the continental United States and across the world.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 22 April 2024 to update the link in the Data Availability Statement for the ALEXI E and CFSR-based downwelling solar radiation data.

Corresponding author: R. D. Koster, randal.d.koster@nasa.gov

Supplementary Materials

    • Supplemental Materials (PDF 2.8560 MB)
Save
  • Akbar, R., D. J. S. Gianotti, K. A. McColl, E. Haghighi, G. D. Salvucci, and D. Entekhabi, 2018a: Estimation of landscape soil water losses from satellite observations of soil moisture. J. Hydrometeor., 19, 871889, https://doi.org/10.1175/JHM-D-17-0200.1.

    • Search Google Scholar
    • Export Citation
  • Akbar, R., D. S. Gianotti, K. A. McColl, E. Haghighi, G. D. Salvucci, and D. Entekhabi, 2018b: Hydrological storage length scales represented by remote sensing estimates of soil moisture and precipitation. Water Resour. Res., 54, 14761492, https://doi.org/10.1002/2017WR021508.

    • Search Google Scholar
    • Export Citation
  • Albergel, C., and Coauthors, 2008: From near-surface to root-zone soil moisture using an exponential filter: An assessment of the method based on in-situ observations and model simulations. Hydrol. Earth Syst. Sci., 12, 13231337, https://doi.org/10.5194/hess-12-1323-2008.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. A. Otkin, and W. P. Kustas, 2007: A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. J. Geophys. Res., 112, D10117, https://doi.org/10.1029/2006JD007506.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., and Coauthors, 2011: Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrol. Earth Syst. Sci., 15, 223239, https://doi.org/10.5194/hess-15-223-2011.

    • Search Google Scholar
    • Export Citation
  • Bateni, S. M., and D. Entekhabi, 2012: Relative efficiency of land surface energy balance components. Water Resour. Res., 48, W04510, https://doi.org/10.1029/2011WR011357.

    • Search Google Scholar
    • Export Citation
  • Brodzik, M. J., B. Billingsley, T. Haran, B. Raup, and M. H. Savoie, 2012: EASE-Grid 2.0: Incremental but significant improvements for Earth-gridded data sets. ISPRS Int. J. Geoinf., 1, 3245, https://doi.org/10.3390/ijgi1010032.

    • Search Google Scholar
    • Export Citation
  • Budyko, M. I., 1974: Climate and Life. Academic Press, 508 pp.

  • Chan, S. K., and Coauthors, 2016: Assessment of the SMAP passive soil moisture product. IEEE Trans. Geosci. Remote Sens., 54, 49945007, https://doi.org/10.1109/TGRS.2016.2561938.

    • Search Google Scholar
    • Export Citation
  • Chaubell, J., and Coauthors, 2021: Regularized dual-channel algorithm for the retrieval of soil moisture and vegetation optical depth from SMAP measurements. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 15, 102114, https://doi.org/10.1109/JSTARS.2021.3123932.

    • Search Google Scholar
    • Export Citation
  • Denissen, J. M. C., A. J. Teuling, M. Reichstein, and R. Orth, 2020: Critical soil moisture derived from satellite observations over Europe. J. Geophys. Res. Atmos., 125, e2019JD031672, https://doi.org/10.1029/2019JD031672.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., R. D. Koster, and Z. Guo, 2006: Do global models properly represent the feedback between land and atmosphere? J. Hydrometeor., 7, 11771198, https://doi.org/10.1175/JHM532.1.

    • Search Google Scholar
    • Export Citation
  • Dong, J., R. Akbar, D. J. S. Gianotti, A. F. Feldman, W. T. Crow, and D. Entekhabi, 2022: Can surface soil moisture information identify evapotranspiration regime transitions? Geophys. Res. Lett., 49, e2021GL097697, https://doi.org/10.1029/2021GL097697.

    • Search Google Scholar
    • Export Citation
  • Dong, J., R. Akbar, A. F. Feldman, D. S. Giannotti, and D. Entekhabi, 2023: Land surfaces at the tipping-point for water and energy balance coupling. Water Resour. Res., 59, e2022WR032472, https://doi.org/10.1029/2022WR032472.

    • Search Google Scholar
    • Export Citation
  • Eagleson, P. S., 1978: Climate, soil, and vegetation: 4. The expected value of annual evapotranspiration. Water Resour. Res., 14, 731739, https://doi.org/10.1029/WR014i005p00731.

    • Search Google Scholar
    • Export Citation
  • Entekhabi, D., and Coauthors, 2010: The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE, 98, 704716, https://doi.org/10.1109/JPROC.2010.2043918.

    • Search Google Scholar
    • Export Citation
  • Feldman, A. F., D. J. Short Gianotti, I. F. Trigo, G. D. Salvucci, and D. Entekhabi, 2019: Satellite‐based assessment of land surface energy partitioning–soil moisture relationships and effects of confounding variables. Water Resour. Res., 55, 10 65710 677, https://doi.org/10.1029/2019WR025874.

    • Search Google Scholar
    • Export Citation
  • Feldman, A. F., and Coauthors, 2023: Remotely sensed soil moisture can capture dynamics relevant to plant water uptake. Water Resour. Res., 59, e2022WR033814, https://doi.org/10.1029/2022WR033814.

    • Search Google Scholar
    • Export Citation
  • Ford, T. W., C. O. Wulff, and S. M. Quiring, 2014a: Assessment of observed and model-derived soil moisture-evaporative fraction relationships over the United States southern Great Plains. J. Geophys. Res. Atmos., 119, 62796291, https://doi.org/10.1002/2014JD021490.

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

    • Search Google Scholar
    • Export Citation
  • Fu, Z., and Coauthors, 2022: Critical soil moisture thresholds of plant water stress in terrestrial ecosystems. Sci. Adv., 8, eabq7827, https://doi.org/10.1126/sciadv.abq7827.

    • Search Google Scholar
    • Export Citation
  • Gallego-Elvira, B., C. M. Taylor, P. P. Harris, D. Ghent, K. L. Veal, and S. S. Folwell, 2016: Global observational diagnosis of soil moisture control on the land surface energy balance. Geophys. Res. Lett., 43, 26232631, https://doi.org/10.1002/2016GL068178.

    • Search Google Scholar
    • Export Citation
  • Good, E. J., D. J. Ghent, C. E. Bulgin, and J. J. Remedios, 2017: A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series. J. Geophys. Res. Atmos., 122, 91859210, https://doi.org/10.1002/2017JD026880.

    • Search Google Scholar
    • Export Citation
  • Haghighi, E., D. J. Short Gianotti, R. Akbar, G. D. Salvucci, and D. Entekhabi, 2018: Soil and atmospheric controls on the land surface energy balance: A generalized framework for distinguishing moisture-limited and energy-limited evaporation regimes. Water Resour. Res., 54, 18311851, https://doi.org/10.1002/2017WR021729.

    • Search Google Scholar
    • Export Citation
  • Jonard, F., A. F. Feldman, D. J. Short Gianotti, and D. Entekhabi, 2022: Observed water and light limitation across global ecosystems. Biogeosciences, 19, 55755590, https://doi.org/10.5194/bg-19-5575-2022.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and P. C. D. Milly, 1997: The interplay between transpiration and runoff formulations in land surface schemes used with atmospheric models. J. Climate, 10, 15781591, https://doi.org/10.1175/1520-0442(1997)010<1578:TIBTAR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., S. D. Schubert, and M. J. Suarez, 2009: Analyzing the concurrence of meteorological droughts and warm periods, with implications for the determination of evaporative regime. J. Climate, 22, 33313341, https://doi.org/10.1175/2008JCLI2718.1.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2011: The second phase of the global land–atmosphere coupling experiment: Soil moisture contributions to subseasonal forecast skill. J. Hydrometeor., 12, 805822, https://doi.org/10.1175/2011JHM1365.1.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., Q. Liu, W. T. Crow, and R. H. Reichle, 2023: Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow. Nat. Commun., 14, 3545, https://doi.org/10.1038/s41467-023-39318-3.

    • Search Google Scholar
    • Export Citation
  • Lei, F., W. T. Crow, T. R. H. Holmes, C. Hain, and M. C. Anderson, 2018: Global investigation of soil moisture and latent heat flux coupling strength. Water Resour. Res., 54, 81968215, https://doi.org/10.1029/2018WR023469.

    • Search Google Scholar
    • Export Citation
  • Manabe, S., 1969: Climate and the ocean circulation. I. The atmospheric circulation and the hydrology of the Earth’s surface. Mon. Wea. Rev., 97, 739774, https://doi.org/10.1175/1520-0493(1969)097<0739:CATOC>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • O’Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, R. Bindlish, and J. Chaubell, 2021: SMAP L2 radiometer half-orbit 36 km EASE-Grid soil moisture, version 8. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 8 February 2024, https://doi.org/10.5067/LPJ8F0TAK6E0.

  • Panwar, A., A. Kleidon, and M. Renner, 2019: Do surface and air temperatures contain similar imprints of evaporative conditions? Geophys. Res. Lett., 46, 38023809, https://doi.org/10.1029/2019GL082248.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Search Google Scholar
    • Export Citation
  • Salvucci, G. D., 2001: Estimating the moisture dependence of root zone water loss using conditionally averaged precipitation. Water Resour. Res., 37, 13571365, https://doi.org/10.1029/2000WR900336.

    • Search Google Scholar
    • Export Citation
  • Schwingshackl, C., M. Hirschi, and S. I. Seneviratne, 2017: Quantifying spatiotemporal variations of soil moisture control on surface energy balance and near-surface air temperature. J. Climate, 30, 71057124, https://doi.org/10.1175/JCLI-D-16-0727.1.

    • Search Google Scholar
    • Export Citation
  • Sehgal, V., N. Gaur, and B. P. Mohanty, 2020: Global surface soil moisture drydown patterns. Water Resour. Res., 57, e2020WR027588, https://doi.org/10.1029/2020WR027588.

    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., and Coauthors, 2006: Soil moisture memory in AGCM simulations: Analysis of Global Land–Atmosphere Coupling Experiment (GLACE) data. J. Hydrometeor., 7, 10901112, https://doi.org/10.1175/JHM533.1.

    • Search Google Scholar
    • Export Citation
  • Shepard, D., 1968: A two-dimensional interpolation function for irregularly-spaced data. ACM ‘68: Proceedings of the 1968 23rd ACM National Conference, Association for Computing Machinery, 517–524, https://doi.org/10.1145/800186.810616.

  • Sud, Y. C., and M. J. Fennessy, 1982: An observational‐data based evapotranspiration function for general circulation models. Atmos.–Ocean, 20, 301316, https://doi.org/10.1080/07055900.1982.9649147.

    • Search Google Scholar
    • Export Citation
  • Trugman, A. T., D. Medvigy, J. S. Mankin, and W. R. L. Anderegg, 2018: Soil moisture stress as a major driver of carbon cycle uncertainty. Geophys. Res. Lett., 45, 64956503, https://doi.org/10.1029/2018GL078131.

    • Search Google Scholar
    • Export Citation
  • van den Hurk, B., F. Doblas-Reyes, G. Balsamo, R. D. Koster, S. I. Seneviratne, and H. Camargo Jr., 2012: Soil moisture effects on seasonal temperature and precipitation forecast scores in Europe. Climate Dyn., 38, 349362, https://doi.org/10.1007/s00382-010-0956-2.

    • Search Google Scholar
    • Export Citation
  • Vargas Zeppetello, L. R., D. S. Battisti, and M. B. Baker, 2019: The origin of soil moisture evaporation “regimes.” J. Climate, 32, 69396960, https://doi.org/10.1175/JCLI-D-19-0209.1.

    • Search Google Scholar
    • Export Citation
  • Wagner, W., G. Lemoine, and H. Rott, 1999: A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sens. Environ., 70, 191207, https://doi.org/10.1016/S0034-4257(99)00036-X.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 932 932 124
Full Text Views 382 382 38
PDF Downloads 363 363 29