Observed and Simulated Soil Moisture Variability over the Lower Mississippi Delta Region

Georgy V. Mostovoy Geosystems Research Institute, Mississippi State University, Mississippi State, Mississippi

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Valentine G. Anantharaj Geosystems Research Institute, Mississippi State University, Mississippi State, Mississippi

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Abstract

To better understand error and spatial variability sources of soil moisture simulated with land surface models, observed and simulated values of soil moisture (using offline simulations with the Noah land surface model with four soil layers and approximately 1-km horizontal grid spacing) were compared. This comparison between observed and modeled daily values of soil moisture was performed over the Lower Mississippi Delta region during summer–fall months 2004–06. The Noah simulations covered the 2.5° × 2.5° latitude–longitude domain and were forced by the North American Land Data Assimilation System (NLDAS) atmospheric forcing fields. Hourly soil moisture measurements and other data, including local meteorological and soil physical properties data from 12 Soil Climate Analysis Network (SCAN) sites, were used. The results show that both the observed and simulated level of soil moisture depend critically on the specified–sampled soil texture. Soil types with a relatively high observed clay content (more than 50% of weight) retain more water as a result of low water diffusivity than silty–sandy soils with 20% or less clay, provided that other conditions are the same. This fact is in agreement with previous studies and implies a strong soil texture control (through related hydraulic parameters) on the accuracy of simulated soil moisture. Sensitivity tests using the Noah model were performed to assess the effect of using the hydraulic parameters related to the site-specific soil texture on soil moisture quality. Indeed, at some SCAN sites, the errors (root-mean-square difference and bias) were reduced. Simulated soil moisture showed at least a 50% reduction when the site-specific soil texture was used in Noah simulations compared to those derived from the State Soil Geographic (STATSGO) data. The most significant improvement of simulated soil moisture was observed within the top 0–10 cm layer where an original positive bias (an excessive wetness) was almost eliminated. Meanwhile, excessive dryness (negative soil moisture bias), which was dominant within the second and third model layers, was also reduced. These improvements are expected to be valid at sites/regions with low (<0.3) vegetation fraction.

Corresponding author address: Dr. Georgy Mostovoy, Geosystems Research Institute, Mississippi State University, P.O. Box 9652, Mississippi State, MS 39762. Email: mostovoi@gri.msstate.edu

Abstract

To better understand error and spatial variability sources of soil moisture simulated with land surface models, observed and simulated values of soil moisture (using offline simulations with the Noah land surface model with four soil layers and approximately 1-km horizontal grid spacing) were compared. This comparison between observed and modeled daily values of soil moisture was performed over the Lower Mississippi Delta region during summer–fall months 2004–06. The Noah simulations covered the 2.5° × 2.5° latitude–longitude domain and were forced by the North American Land Data Assimilation System (NLDAS) atmospheric forcing fields. Hourly soil moisture measurements and other data, including local meteorological and soil physical properties data from 12 Soil Climate Analysis Network (SCAN) sites, were used. The results show that both the observed and simulated level of soil moisture depend critically on the specified–sampled soil texture. Soil types with a relatively high observed clay content (more than 50% of weight) retain more water as a result of low water diffusivity than silty–sandy soils with 20% or less clay, provided that other conditions are the same. This fact is in agreement with previous studies and implies a strong soil texture control (through related hydraulic parameters) on the accuracy of simulated soil moisture. Sensitivity tests using the Noah model were performed to assess the effect of using the hydraulic parameters related to the site-specific soil texture on soil moisture quality. Indeed, at some SCAN sites, the errors (root-mean-square difference and bias) were reduced. Simulated soil moisture showed at least a 50% reduction when the site-specific soil texture was used in Noah simulations compared to those derived from the State Soil Geographic (STATSGO) data. The most significant improvement of simulated soil moisture was observed within the top 0–10 cm layer where an original positive bias (an excessive wetness) was almost eliminated. Meanwhile, excessive dryness (negative soil moisture bias), which was dominant within the second and third model layers, was also reduced. These improvements are expected to be valid at sites/regions with low (<0.3) vegetation fraction.

Corresponding author address: Dr. Georgy Mostovoy, Geosystems Research Institute, Mississippi State University, P.O. Box 9652, Mississippi State, MS 39762. Email: mostovoi@gri.msstate.edu

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  • Anantharaj, V., Mostovoy G. V. , Peters-Lidard C. D. , Houser P. R. , and Li B. , 2007: An initial assessment of soil moisture fields simulated by the Noah Land Surface Model at regional and local scales. Eos, Trans. Amer. Geophys. Union, 88 .(Joint Assembly Suppl.), Abstract H51D-02.

    • Search Google Scholar
    • Export Citation
  • Baker, F. G., 1978: Variability of hydraulic conductivity within and between nine Wisconsin soil series. Water Resour. Res., 14 , 103108.

  • Black, T. L., 1994: The new NMC mesoscale eta model: Description and forecast examples. Wea. Forecasting, 9 , 265278.

  • Bosilovich, M. G., and Sun W-Y. , 1995: Formulation and verification of a land surface parameterization for atmospheric models. Bound.-Layer Meteor., 73 , 321341.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., and Sun W-Y. , 1998: Monthly simulation of surface layer fluxes and soil properties during FIFE. J. Atmos. Sci., 55 , 11701184.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Braun, F. J., and Schädler G. , 2005: Comparison of soil hydraulic parameterizations for mesoscale meteorological models. J. Appl. Meteor., 44 , 11161132.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brock, F. V., Crawford K. C. , Elliot R. L. , Cupers G. W. , Stadler S. J. , Johnson H. L. , and Eilts M. D. , 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol., 12 , 519.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brutsaert, W., 2005: Hydrology: An Introduction. Cambridge University Press, 605 pp.

  • Capehart, W. J., and Carlson T. N. , 1994: Estimating near-surface soil moisture availability using a meteorologically driven soil-water profile model. J. Hydrol., 160 , 120.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and Dudhia J. , 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and Coauthors, 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101 , 72517268.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and Coauthors, 2007: Description and evaluation of the characteristics of the NCAR High-Resolution Land Data Assimilation System. J. Appl. Meteor. Climatol., 46 , 694713.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clapp, R. B., and Hornberger G. M. , 1978: Empirical equations for some soil hydraulic properties. Water Resour. Res., 14 , 601604.

  • Cosby, B. J., Hornberger G. M. , Clapp R. B. , and Ginn T. R. , 1984: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res., 20 , 682690.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cosgrove, B. A., and Coauthors, 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108 .8842, doi:10.1029/2002JD003118.

    • Search Google Scholar
    • Export Citation
  • Crawford, T. D., Stensrud D. J. , Carlson T. N. , and Capehart W. J. , 2000: Using a soil hydrology model to obtain regionally averaged soil moisture values. J. Hydrometeor., 1 , 353363.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cuenca, R. H., Ek M. , and Marht L. , 1996: Impact of soil water property parameterization on atmospheric boundary layer simulation. J. Geophys. Res., 101 , 72697277.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deardorff, J. W., 1978: Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. J. Geophys. Res., 83 , 18891903.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ek, M., and Cuenca R. H. , 1994: Variation in soil parameters: Implications for modeling surface fluxes and atmospheric boundary-layer development. Bound.-Layer Meteor., 70 , 369383.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ek, M. B., Mitchell K. E. , Lin Y. , Rogers E. , Grunmann P. , Koren V. , Gayno G. , and Tarpley J. D. , 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta Model. J. Geophys. Res., 108 .8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Gutman, G., and Ignatov A. , 1998: The derivation of green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens., 19 , 15331543.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hillel, D., 2004: Introduction to Environmental Soil Physics. Elsevier Academic Press, 494 pp.

  • Hogue, T. S., Bastidas L. , Gupta H. , Sorooshian S. , Mitchell K. , and Emmerich W. , 2005: Evaluation and transferability of the Noah Land Surface Model in semiarid environments. J. Hydrometeor., 6 , 6884.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hollinger, S. E., and Isard S. A. , 1994: A soil moisture climatology of Illinois. J. Climate, 7 , 822833.

  • Irannejad, P., and Shao Y. , 1998: Description and validation of the Atmosphere–Land–Surface Interaction Scheme (ALSIS) with HAPEX and Cabauw data. Global Planet. Change, 19 , 87114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kabat, P., Hutjes R. W. A. , and Feddes R. A. , 1997: The scaling characteristics of soil parameters: From plot scale heterogeneity to subgrid parameterization. J. Hydrol., 190 , 363396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, S. V., and Coauthors, 2006: Land Information System—An interoperable framework for high resolution land surface modeling. Environ. Modell. Software, 21 , 14021415.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liang, X., Wood E. F. , and Lettenmaier D. P. , 1996: Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification. Global Planet. Change, 13 , 195206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, L., and Coauthors, 2003: Validation of the North American Land data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains. J. Geophys. Res., 108 .8843, doi:10.1029/2002JD003246.

    • Search Google Scholar
    • Export Citation
  • Luo, Y., and Houser P. R. , 2008: Soil moisture data assimilation with Noah land surface model using the Land Information System (LIS). George Mason University, CREW Tech. Rep. CREW-TR-2008-04, 14 pp.

  • Mahrt, L., and Ek M. , 1984: The influence of atmospheric stability on potential evaporation. J. Climate Appl. Meteor., 23 , 222234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahrt, L., and Pan H-L. , 1984: A two-layer model of soil hydrology. Bound.-Layer Meteor., 29 , 120.

  • Marshall, C. H., Crawford K. C. , Mitchell K. E. , and Stensrud D. J. , 2003: The impact of the land surface physics in the operational NCEP Eta Model on simulating the diurnal cycle: Evaluation and testing using Oklahoma Mesonet data. Wea. Forecasting, 18 , 748768.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, D. A., and White R. A. , 1998: A conterminous United States multilayer soil characteristics data set for regional climate and hydrology modeling. Earth Interactions, 2 . [Available online at http://EarthInteractions.org.].

    • Search Google Scholar
    • Export Citation
  • Mohr, K. I., Famiglietti J. S. , Boone A. , and Starks P. J. , 2000: Modeling soil moisture and surface flux variability with an untuned land surface scheme: A case study from the Southern Great Plains 1997 Hydrology Experiment. J. Hydrometeor., 1 , 154169.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mostovoy, G. V., Anantharaj V. , Houser P. R. , and Peters-Lidard C. D. , 2007: Use of SCAN observations for validation of soil moisture spatial distribution simulated by the Land-Surface Model over the lower Mississippi Delta region. Preprints, 11th Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land-Surface (IOAS-AOLS), San Antonio, TX, Amer. Meteor. Soc., P2.3. [Available online at http://ams.confex.com/ams/pdfpapers/118246.pdf.].

  • Mualem, Y., 1976: A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res., 12 , 513522.

  • National Resources Conservation Service, cited. 2007: Soil Climate Analysis Network (SCAN). [Available online at http:// www.wcc.nrcs.usda.gov/scan.].

  • Pan, H-L., and Mahrt L. , 1987: Interaction between soil hydrology and boundary layer development. Bound.-Layer Meteor., 38 , 185202.

  • Peters-Lidard, C. D., Kumar S. V. , Tian Y. , Eastman J. L. , and Houser P. R. , 2004: Global urban-scale land–atmosphere modeling with the land information system. Preprints, Symp. on Planning, Nowcasting, and Forecasting in the Urban Zone, Seattle, WA, Amer. Meteor. Soc., 4.1. [Available online at http://ams.confex.com/ams/pdfpapers/73726.pdf.].

  • Rawls, W. J., Brakensiek D. L. , and Saxton K. E. , 1982: Estimation of soil water properties. Trans. ASAE, 25 , 13161320.

  • Reichle, R. H., and Koster R. D. , 2004: Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett., 31 .L19501, doi:10.1029/2004GL020938.

    • Search Google Scholar
    • Export Citation
  • Richter, H., Western A. W. , and Chiew F. H. , 2004: The effect of soil and vegetation parameters in the ECMWF land surface scheme. J. Hydrometeor., 5 , 11311146.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robock, A., and Coauthors, 2003: Evaluation of the North American Land Data Assimilation System over the southern Great Plains during the warm season. J. Geophys. Res., 108 .8846, doi:10.1029/2002JD003245.

    • Search Google Scholar
    • Export Citation
  • Rodríguez-Iturbe, I., Vogel G. K. , Rigon R. , Entekabi D. , Castelli F. , and Rinaldo A. , 1995: On the spatial organization of soil moisture fields. Geophys. Res. Lett., 22 , 27572760.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santanello J. A. Jr., , Peters-Lidard C. , Garcia M. E. , Macko D. M. , Tischler M. A. , Moran M. S. , and Thoma D. P. , 2007: Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed. Remote Sens. Environ., 110 , 7997.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shao, Y., and Henderson-Sellers A. , 1996: Modeling soil moisture: A project for Intercomparison of Land Surface Parameterization Schemes phase 2(b). J. Geophys. Res., 101 , 72277250.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shao, Y., and Irannejad P. , 1999: On the choice of soil hydraulic models in land-surface schemes. Bound.-Layer Meteor., 90 , 83115.

  • Smirnova, T. G., Brown J. M. , and Benjamin S. G. , 1997: Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon. Wea. Rev., 125 , 18701884.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, R. E., 2002: Infiltration Theory for Hydrologic Applications. Amer. Geophys. Union, 212 pp.

  • Sridhar, V., Elliot R. L. , Chen F. , and Brotzge J. A. , 2002: Validation of the NOAH-OSU land surface model using surface flux measurements in Oklahoma. J. Geophys. Res., 107 .4418, doi:10.1029/2001JD001306.

    • Search Google Scholar
    • Export Citation
  • Stevens Water Monitoring System, Inc., 2007: The Hydra Probe Soil Sensor users manual, 63 pp. [Available online at http://www.stevenswater.com/catalog/products/soil_sensors/manual/Hydra%20Probe%20Manual%2092915%20July%202007.pdf.].

  • Tischler, M., Garcia M. , Peters-Lidard C. , Moran M. S. , Miller S. , Thoma D. , Kumar S. , and Geiger J. , 2007: A GIS framework for surface-layer soil moisture estimation combining satellite radar measurements and land surface modeling with soil physical property estimation. Environ. Modell. Software, 22 , 891898.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Genuchten, M. T., 1980: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Amer. J., 44 , 892898.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viterbo, P., and Beljaars A. C. M. , 1995: An improved land surface parameterization scheme in the ECMWF model and its validation. J. Climate, 8 , 27162748.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walko, R. L., and Coauthors, 2000: Coupled atmosphere–biophysics–hydrology models for environmental modeling. J. Appl. Meteor., 39 , 931944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, K., Koike T. , Ye B. , and Bastidas L. , 2005: Inverse analysis of the role of soil vertical heterogeneity in controlling surface soil state and energy partition. J. Geophys. Res., 110 .D08101, doi:10.1029/2004JD005500.

    • Search Google Scholar
    • Export Citation
  • Yildiz, O., and Barros A. P. , 2007: Elucidating vegetation controls on the hydroclimatology of a mid-latitude basin. J. Hydrol., 333 , 431448.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoo, C., Valdes J. B. , and North G. R. , 1998: Evaluation of the impact of rainfall on soil moisture variability. Adv. Water Resour., 21 , 375384.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhai, Y., Thomasson J. A. , Boggess J. E. , and Sui R. , 2006: Soil texture classification with artificial neural networks operating on remote sensing data. Comput. Electron. Agric., 54 , 5368.

    • Crossref
    • Search Google Scholar
    • Export Citation
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