Editorial Type:
Article
Article Type:
Research Article

Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing

C. Albergel * European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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W. Dorigo Department of Geodesy and Geo-Information, Vienna University of Technology, Vienna, Austria

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R. H. Reichle Global Modeling and Assimilation Office, NASA Goddard Space Flight Centre, Greenbelt, Maryland

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G. Balsamo * European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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P. de Rosnay * European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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J. Muñoz-Sabater * European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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L. Isaksen * European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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R. de Jeu Department of Earth Sciences, Faculty of Earth and Life Sciences, VU University Amsterdam, Amsterdam, Netherlands

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W. Wagner Department of Geodesy and Geo-Information, Vienna University of Technology, Vienna, Austria

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Print Publication:
01 Aug 2013
Collections:
NASA Soil Moisture Active Passive (SMAP) – Pre-launch Applied Research
DOI:
https://doi.org/10.1175/JHM-D-12-0161.1
Page(s):
1259–1277
Restricted access

Abstract

In situ soil moisture measurements from 2007 to 2010 for 196 stations from five networks across the world (United States, France, Spain, China, and Australia) are used to determine the reliability of three soil moisture products: (i) a revised version of the ECMWF Interim Re-Analysis (ERA-Interim; ERA-Land); (ii) a revised version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis from NASA (MERRA-Land); and (iii) a new, microwave-based multisatellite surface soil moisture dataset (SM-MW). Evaluation of the time series and anomalies from a moving monthly mean shows a good performance of the three products in capturing the annual cycle of surface soil moisture and its short-term variability. On average, correlations (95% confidence interval) are 0.66 (±0.038), 0.69 (±0.038), and 0.60 (±0.061) for ERA-Land, MERRA-Land, and SM-MW. The two reanalysis products also capture the root-zone soil moisture well; on average, correlations are 0.68 (±0.035) and 0.73 (±0.032) for ERA-Land and MERRA-Land, respectively. Global trends analysis for 1988–2010 suggests a decrease of surface soil moisture contents (72% of significant trends are negative, i.e., drying) for ERA-Land and an increase in surface soil moisture (59% of significant trends are positive, i.e., wetting) for MERRA-Land. As the spatial extent and fractions of significant trends in both products differ, the trend reflected in the majority of grid points within different climate classes was investigated and compared to that of SM-MW. The latter is dominated by negative significant trends (73.2%) and is more in line with ERA-Land. For both reanalysis products, trends for the upper layer of soil are confirmed in the root-zone soil moisture (first meter of soil).

Corresponding author address: Clément Albergel, European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, United Kingdom. E-mail: clement.albergel@ecmwf.int

This article is included in the NASA Soil Moisture Active Passive (SMAP) – Pre-launch Applied Research Special Collection.

Abstract

In situ soil moisture measurements from 2007 to 2010 for 196 stations from five networks across the world (United States, France, Spain, China, and Australia) are used to determine the reliability of three soil moisture products: (i) a revised version of the ECMWF Interim Re-Analysis (ERA-Interim; ERA-Land); (ii) a revised version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis from NASA (MERRA-Land); and (iii) a new, microwave-based multisatellite surface soil moisture dataset (SM-MW). Evaluation of the time series and anomalies from a moving monthly mean shows a good performance of the three products in capturing the annual cycle of surface soil moisture and its short-term variability. On average, correlations (95% confidence interval) are 0.66 (±0.038), 0.69 (±0.038), and 0.60 (±0.061) for ERA-Land, MERRA-Land, and SM-MW. The two reanalysis products also capture the root-zone soil moisture well; on average, correlations are 0.68 (±0.035) and 0.73 (±0.032) for ERA-Land and MERRA-Land, respectively. Global trends analysis for 1988–2010 suggests a decrease of surface soil moisture contents (72% of significant trends are negative, i.e., drying) for ERA-Land and an increase in surface soil moisture (59% of significant trends are positive, i.e., wetting) for MERRA-Land. As the spatial extent and fractions of significant trends in both products differ, the trend reflected in the majority of grid points within different climate classes was investigated and compared to that of SM-MW. The latter is dominated by negative significant trends (73.2%) and is more in line with ERA-Land. For both reanalysis products, trends for the upper layer of soil are confirmed in the root-zone soil moisture (first meter of soil).

Corresponding author address: Clément Albergel, European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, United Kingdom. E-mail: clement.albergel@ecmwf.int

This article is included in the NASA Soil Moisture Active Passive (SMAP) – Pre-launch Applied Research Special Collection.

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  • 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, 1323–1337, doi:10.5194/hess-12-1323-2008.

    • Search Google Scholar
    • Export Citation

  • Albergel, C., Rüdiger C. , Carrer D. , Calvet J.-C. , Fritz N. , Naeimi V. , Bartalis Z. , and Hasenauer S. , 2009: An evaluation of ASCAT surface soil moisture products with in situ observations in southwestern France. Hydrol. Earth Syst. Sci., 13, 115–124, doi:10.5194/hess-13-115-2009.

    • Search Google Scholar
    • Export Citation

  • Albergel, C., and Coauthors, 2010: Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France. Hydrol. Earth Syst. Sci., 14, 2177–2191, doi:10.5194/hess-14-2177-2010.

    • Search Google Scholar
    • Export Citation

  • Albergel, C., Balsamo G. , de Rosnay P. , Muñoz-Sabater J. , and Boussetta S. , 2012a: A bare ground evaporation revision in the ECMWF land-surface scheme: Evaluation of its impact using ground soil moisture and satellite microwave data. Hydrol. Earth Syst. Sci., 16, 3607–3620, doi:10.5194/hess-16-3607-2012.

    • Search Google Scholar
    • Export Citation

  • Albergel, C., de Rosnay P. , Balsamo G. , Isaksen L. , and Muñoz Sabater J. , 2012b: Soil moisture analyses at ECMWF: Evaluation using global ground-based in situ observations. J. Hydrometeor., 13, 1442–1460.

    • Search Google Scholar
    • Export Citation

  • Albergel, C., de Rosnay P. , Gruhier C. , Muñoz-Sabater J. , Hasenauer S. , Isaksen L. , Kerr Y. , and Wagner W. , 2012c: Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations. Remote Sens. Environ., 118, 215–226, doi:10.1016/j.rse.2011.11.017.

    • Search Google Scholar
    • Export Citation

  • Balsamo, G., Viterbo P. , Beljaars A. C. M. , van den Hurk B. J. J. M. , Hirschi M. , Betts A. K. , and Scipal K. , 2009: A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the ECMWF-IFS. J. Hydrometeor., 10, 623–643.

    • Search Google Scholar
    • Export Citation

  • Balsamo, G., and Coauthors, 2012: ERA-Interim/Land: A global land-surface reanalysis based on ERA-Interim meteorological forcing. ERA Rep. 13, 25 pp. [Available online at http://www.ecmwf.int/publications/library/ecpublications/_pdf/era/era_report_series/RS_13.pdf.]

  • Bartalis, Z., Wagner W. , Naeimi V. , Hasenauer S. , Scipal K. , Bonekamp H. , Figa J. , and Anderson C. , 2007: Initial soil moisture retrievals from the METOP-A Advanced Scatterometer (ASCAT). Geophys. Res. Lett., 34, L20401, doi:10.1029/2007GL031088.

    • Search Google Scholar
    • Export Citation

  • Berrisford, P., Dee D. P. , Fielding K. , Fuentes M. , Kallberg P. , Kobayashi S. , and Uppala S. M. , 2009: The ERA-Interim archive. ERA Rep. 1, 16 pp. [Available online at http://www.ecmwf.int/publications/library/ecpublications/_pdf/era/era_report_series/RS_1.pdf.]

  • Boussetta, S., Balsamo G. , Beljaars A. , and Jarlan J. , 2010: Impact of a satellite-derived Leaf Area Index monthly climatology in a global Numerical Weather Prediction model. ECMWF Tech. Memo. 640, 28 pp.

  • Brocca, L., and Coauthors, 2011: Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe. Remote Sens. Environ., 115, 3390–3408.

    • Search Google Scholar
    • Export Citation

  • Burn, D. H., and Hag Elnur M. A. , 2002: Detection of hydrologic trends and variability. J. Hydrol., 255, 107–122.

  • Calvet, J.-C., Fritz N. , Froissard F. , Suquia D. , Petitpa A. , and Piguet B. , 2007: In situ soil moisture observations for the CAL/VAL of SMOS: The SMOSMANIA network. Proc. Int. Geoscience and Remote Sensing Symp., Barcelona, Spain, IGARSS, doi:10.1109/IGARSS.2007.4423019.

    • Search Google Scholar
    • Export Citation

  • Calvet, J.-C., Wigneron J.-P. , Walker J. P. , Karbou F. , Chanzy A. , and Albergel C. , 2011: Sensitivity of passive microwave observations to soil moisture and vegetation water content: L-band to W-band. IEEE Trans. Geosci. Remote Sens., 49, 1190–1199, doi:10.1109/TGRS.2010.2050488.

    • Search Google Scholar
    • Export Citation

  • Ceballos, A., Scipal K. , Wagner W. , and Martinez-Fernandez J. , 2005: Validation of ERS scatterometer-derived soil moisture data in the central part of the Duero-Basin, Spain. Hydrol. Processes, 19, 1549–1566.

    • Search Google Scholar
    • Export Citation

  • de Rosnay, P., Balsamo G. , Albergel C. , Muñoz-Sabater J. , and Isaksen L. , 2013a: Initialisation of land surface variables for numerical weather prediction. Surv. Geophys., doi:10.1007/s10712-012-9207-x, in press.

    • Search Google Scholar
    • Export Citation

  • de Rosnay, P., Drusch M. , Vasiljevic D. , Balsamo G. , Albergel C. , and Isaksen L. , 2013b: A simplified Extended Kalman Filter for the global operational soil moisture analysis at ECMWF. Quart. J. Roy. Meteor. Soc.,doi:10.1002/qj.2023, in press.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation

  • Dente, L., Vekerdy Z. , Wen J. , and Su Z. , 2012: Maqu network for validation of satellite-derived soil moisture products. Int. J. Appl. Earth Obs. Geoinf., 17, 55–65.

    • Search Google Scholar
    • Export Citation

  • Dharssi, I., Bovis K. , Macpherson B. , and Jones C. , 2011: Operational assimilation of ASCAT surface soil wetness at the Met Office. Hydrol. Earth Syst. Sci., 15, 2729–2746, doi:10.5194/hess-15-2729-2011.

    • Search Google Scholar
    • Export Citation

  • Dole, R., and Coauthors, 2011: Was there a basis for anticipating the 2010 Russian heat wave? Geophys. Res. Lett., 38, L06702, doi:10.1029/2010GL046582.

    • Search Google Scholar
    • Export Citation

  • Dorigo, W. A., Scipal K. , Parinussa R. M. , Liu Y. Y. , Wagner W. , de Jeu R. A. M. , and Naeimi V. , 2010: Error characterisation of global active and passive microwave soil moisture datasets. Hydrol. Earth Syst. Sci., 14, 2605–2616, doi:10.5194/hess-14-2605-2010.

    • Search Google Scholar
    • Export Citation

  • Dorigo, W. A., and Coauthors, 2011: The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci., 15, 1675–1698, doi:10.5194/hess-15-1675-2011.

    • Search Google Scholar
    • Export Citation

  • Dorigo, W. A., de Jeu R. , Chung D. , Parinussa R. , Liu Y. , Wagner W. , and Fernández-Prieto D. , 2012: Evaluating global trends (1988–2010) in harmonized multi-satellite soil moisture data. Geophys. Res. Lett.,39, L18405, doi:10.1029/2012GL052988.

  • Dorigo, W. A., Xaver A. , Vreugdenhil M. , Gruber A. , Hegyiová A. , Sanchis-Dufau A. D. , Wagner W. , and Drusch M. , 2013: Global automated quality control of in-situ soil moisture data from the International Soil Moisture Network. Vadose Zone J., doi:10.2136/vzj2012.0097, in press.

    • Search Google Scholar
    • Export Citation

  • Douville, H., Viterbo P. , Mahfouf J.-F. , and Beljaars A. C. M. , 2000: Evaluation of the optimum interpolation and nudging techniques for soil moisture analysis using FIFE data. Mon. Wea. Rev., 128, 1733–1756.

    • Search Google Scholar
    • Export Citation

  • Draper, C. S., Reichle R. H. , De Lannoy G. J. M. , and Liu Q. , 2012: Assimilation of passive and active microwave soil moisture retrievals. Geophys. Res. Lett., 39, L04401, doi:10.1029/2011GL050655.

    • Search Google Scholar
    • Export Citation

  • Dutra, E., Balsamo G. , Viterbo P. , Miranda P. M. A. , Beljaars A. , Schär C. , and Elder K. , 2010: An improved snow scheme for the ECMWF land surface model: Description and offline validation. J. Hydrometeor., 11, 899–916.

    • Search Google Scholar
    • Export Citation

  • Entekhabi, D., Reichle R. H. , Koster R. D. , and Crow W. T. , 2010: Performance metrics for soil moisture retrieval and application requirements. J. Hydrometeor., 11, 832–840.

    • Search Google Scholar
    • Export Citation

  • FAO, 2003: Digital soil map of the world (DSMW). Tech.Rep., Food and Agriculture Organization of the United Nations, 50 pp.

  • FAO, cited 2009: Harmonized World Soil Database version 1.1. FAO, Rome, Italy. [Available online at http://www.fao.org/nr/land/soils/harmonized-world-soil-database/en/.]

  • Geiger, R., 1954: Klassifikation der Klimate nach W. Köppen. Landolt-Börnstein: Zahlenwerte und Funktionen aus Physik, Chemie, Astronomie, Geophysik und Technik, Alte Serie, Vol. 3, Springer, 603–607.

  • Hirsch, R. M., Slack J. R. , and Smith R. A. , 1982: Techniques of trend analysis for monthly water-quality data. Water Resour. Res., 18, 107–121.

    • Search Google Scholar
    • Export Citation

  • Jung, M., and Coauthors, 2010: Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467, 951–954, doi:10.1038/nature09396.

    • Search Google Scholar
    • Export Citation

  • Kendall, M. G., 1975: Rank Correlation Methods. Charles Griffin, 202 pp.

  • Kerr, Y. H., and Coauthors, 2010: The SMOS Mission: New tool for monitoring key elements of the global water cycle. Proc. IEEE,98, 666–687.

  • Köppen, W., 1900: Versuch einer Klassifikation der Klimate, vorzugsweise nach ihren Beziehungen zur Pflanzenwelt. Geogr. Z.,6, 657–679.

  • Kottek, M., Grieser J. , Beck C. , Rudolf B. , and Rubel F. , 2006: World Map of the Köppen-Geiger climate classification updated. Meteor. Z., 15, 259–263, doi:10.1127/0941-2948/2006/0130.

    • Search Google Scholar
    • Export Citation

  • Liu, Y. Y., Parinussa R. M. , Dorigo W. A. , De Jeu R. A. M. , Wagner W. , van Dijk A. I. J. M. , McCabe M. F. , and Evans J. P. , 2011: Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol. Earth Syst. Sci., 15, 425–436, doi:10.5194/hess-15-425-2011.

    • Search Google Scholar
    • Export Citation

  • Liu, Y. Y., Dorigo W. A. , Parinussa R. M. , De Jeu R. A. M. , Wagner W. , McCabe M. F. , Evans J. P. , and van Dijk A. I. J. M. , 2012: Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens. Environ., 123, 280–297, doi:10.1016/j.rse.2012.03.014.

    • Search Google Scholar
    • Export Citation

  • Liu, Y. Y., van Dijk A. I. J. M. , McCabe M. , Evans J. , and de Jeu R. A. M. , 2013: Global vegetation biomass change (1988–2008) and attribution to environmental and human drivers. Global Ecol. Biogeogr.,22, 692–705, doi:10.1111/geb.12024.

  • Mahfouf, J.-F., 1991: Analysis of soil moisture from near surface parameters: A feasibility study. J. Appl. Meteor., 30, 506–526.

  • Mann, H. B., 1945: Non-parametric test against trend. Econometrika, 13, 245–259.

  • Molteni, F., and Coauthors, 2011: The new ECMWF seasonal forecast system (System 4). ECMWF Tech. Memo 656, 51 pp.

  • Parrens, M., Zakharova E. , Lafont S. , Calvet J.-C. , Kerr Y. , Wagner W. , and Wigneron J.-P. , 2012: Comparing soil moisture retrievals from SMOS and ASCAT over France. Hydrol. Earth Syst. Sci., 16, 423–440.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., 2012: The MERRA-Land Data Product. GMAO Office Note 3, XX pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/Reichle541.pdf.].

  • Reichle, R. H., Koster R. D. , De Lannoy G. J. M. , Forman B. A. , Liu Q. , Mahanama S. P. P. , and Toure A. , 2011: Assessment and enhancement of MERRA land surface hydrology estimates. J. Climate, 24, 6322–6338.

    • 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, 3624–3648.

    • Search Google Scholar
    • Export Citation

  • Robertson, F. R., Bosilovich M. , Chen J. , and Miller T. , 2011: The effect of satellite observing system changes on MERRA water and energy fluxes. J. Climate, 24, 5197–5217.

    • Search Google Scholar
    • Export Citation

  • Sánchez, N., Martínez-Fernández J. , Scaini A. , and Pérez-Gutiérrez C. , 2012: Validation of the SMOS L2 soil moisture data in REMEDHUS network (Spain). IEEE Trans. Geosci. Remote Sens., 50, 1602–1611.

    • Search Google Scholar
    • Export Citation

  • Schaefer, G. L., and Paetzold R. F. , 2000: SNOTEL (SNOwpack TELemetry) and SCAN (Soil Climate Analysis Network). Automated Weather Stations for Applications in Agriculture and Water Resources Management, K. G. Hubbard and M. V. K. Sivakumar, Eds., WMO/TD-1074, World Meteorological Organization, 187–194. [Available online at www.wamis.org/agm/pubs/agm3/WMO-TD1074.pdf.]

  • Schmugge, T. J., 1983: Remote sensing of soil moisture: Recent advances. IEEE Trans. Geosci. Remote Sens., GE21, 145–146.

  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall's tau. J. Amer. Stat. Assoc., 63, 1379–1389.

  • Sheffield, J., and Wood E. F. , 2008: Global trends and variability in soil moisture and drought characteristics, 1950–2000, from observation-driven simulations of the terrestrial hydrologic cycle. J. Climate, 21, 432–458.

    • Search Google Scholar
    • Export Citation

  • Simmons, A. J., Willet K. M. , Jones P. D. , Thorne P. W. , and Dee D. P. , 2010: Low-frequency variations in surface atmospheric humidity, temperature and precipitation: Inferences from reanalysis and monthly gridded observational datasets. J. Geophys. Res., 115, D01110, doi:10.1029/2009JD012442.

    • Search Google Scholar
    • Export Citation

  • Smith, A. B., and Coauthors, 2012: The Murrumbidgee soil moisture monitoring network data set. Water Resour. Res., 48, W07701, doi:10.1029/2012WR011976.

    • Search Google Scholar
    • Export Citation

  • Stanski, H. R., Wilson L. J. , and Burrows W. R. , 1989: Survey of common verifications methods in meteorology. WMO World Weather Watch Tech. Rep. 8/WMO/TD-358, 114 pp.

  • Su, Z., Wen J. , Dente L. , van der Veldel R. , Wang L. , Ma Y. , Yang K. , and Hu Z. , 2011: The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products. Hydrol. Earth Syst. Sci., 15, 2303–2316, doi:10.5194/hess-15-2303-2011.

    • Search Google Scholar
    • Export Citation

  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 7183–7192.

  • van den Hurk, B. J. J. M., and Viterbo P. , 2003: The Torne-Kalix PILPS 2(e) experiment as a test bed for modifications to the ECMWF land surface scheme. Global Planet. Change, 38, 165–173.

    • Search Google Scholar
    • Export Citation

  • van den Hurk, B. J. J. M., Viterbo P. , Beljaars A. C. M. , and Betts A. K. , 2000: Offline validation of the ERA-40 surface scheme. ECMWF Tech. Memo. 295, 43 pp.

  • Verdon-Kidd, D. C., and Kiem A. S. , 2009: Nature and causes of protracted droughts in southeast Australia: Comparison between the Federation, WWII, and Big Dry droughts. Geophys. Res. Lett., 36, L22707, doi:10.1029/2009GL041067.

    • Search Google Scholar
    • Export Citation

  • Wagner, W., Naeimi V. , Scipal K. , de Jeu R. , and Martinez-Fernandez J. , 2007: Soil moisture from operational meteorological satellite. Hydrogeol. J., 15, 121–113.

    • Search Google Scholar
    • Export Citation

  • Wagner, W., Dorigo W. , de Jeu R. , Fernandez D. , Benveniste J. , Haas E. , and Ertl M. , 2012: Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. Proc. XXII ISPRS Congress, Melbourne, Australia, ISPRS, 315–321.

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