Predicting root zone soil moisture using              surface data           Salvatore Manfreda1, Luca Brocca2, Tommaso Mo...
Predicting root zone soil moisture usingsurface data: Outlineo    The role of soil moisture;o    An analytical relationshi...
Role of Soil MoistureCharacterizing the dynamics of soil moisture is a key issue inhydrology, offering an avenue to improv...
Exponential filter (Wagner et al., RSE 1999)The water balance equation                                                    ...
Use of the exponential filterThe recursive formulation of the method relies on (Albergel et al.,2009):                    ...
Temporal dynamics of soil moisture andAMSU SWI*Time series of daily rainfall (A). Comparison between the soilmoisture (m3 ...
Water flux exchange between the surface and the lower layer The most relevant water mass exchange between the two layers i...
Soil water balanceThe soil water balance can be described by the following expression                                     ...
Soil Moisture Analytical Relationship (SMAR)beetween surface and root zone soil moistureAssuming an initial condition for ...
Soil moisture patterns obtained by hydrologicalsimulationsThe North American Land Data Assimilation System (NLDAS)[Mitchel...
Soil moisture patterns obtained by hydrologicalsimulations Description of the NLDAS domain: DEM, vegetation fraction, soil...
Hydrological Simulation: VIC model                                                              Hydrologic Model (Liang et...
SMAR Application to NLDAS data baseComparison between the relative saturation at 10cm and 100cmdepth and the filtered valu...
SMAR Application to NLDAS data baseCorrelation coefficient between the filtered soil moisture and thesimulated soil moistu...
SMAR Application to NLDAS databaseBox-plot of the correlation between S100SMAR and S100 as afunction of rainfall character...
SMAR Application to NLDAS databaseA) Values of correlation between S100SMAR and S100 as a function of   the variance of S1...
Nature is Complex, butsimple models may suffice. (J. Sprott)    Definition of a feasible mathematical characterization of ...
Papers related to this research line…Manfreda, S., L. Brocca, T. Moramarco, F. Melone, and J. Sheffield, A physically base...
Thanks...European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012.     19
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Predicting Root Zone Soil Moisture using Surface Data

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Predicting Root Zone Soil Moisture using Surface Data

  1. 1. Predicting root zone soil moisture using surface data Salvatore Manfreda1, Luca Brocca2, Tommaso Moramarco2, Florisa Melone2, Justin Sheffield3, and Mauro Fiorentino1 (1) Department of Environmental Engineering and Physics, University of Basilicata, Potenza, Italy. (2) Research Institute for Geo-Hydrological Protection (IRPI), CNR, Perugia, Italy. (3) Department of Civil and Environmental Engineering, Princeton University, Princeton, USA. e-mail: salvatore.manfreda@unibas.it European Geosciences Union Session: HS6.2: Remote sensing of soil moisture General Assembly 2012 Vienna | Austria | 22 – 27 April 2012European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 1
  2. 2. Predicting root zone soil moisture usingsurface data: Outlineo The role of soil moisture;o An analytical relationship between the root zone and surface soil moisture;o The study area - NLDAS database;o Application and validation of the proposed procedure;o Conclusion.European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 2
  3. 3. Role of Soil MoistureCharacterizing the dynamics of soil moisture is a key issue inhydrology, offering an avenue to improve our understanding ofcomplex land surface–atmosphere interactions. Soil moisture, thusrepresent a key variable in several fields:• Numerical Weather Forecasting• Climate Prediction• Shallow Landslide Forecasting• Flood Prediction and Forecasting• Agriculture and Plant Production• Ecological patternsEuropean Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 3
  4. 4. Exponential filter (Wagner et al., RSE 1999)The water balance equation (1)where W is the volumetric wetness of the reservoir, Ws of thesurface, t is the time, L is the depth of the reservoir layer, and C isa pseudodiffusivity coefficient that depends on the soil properties.The solution of equation 1 is Ws(t) (2) W(t) where T=L/CEuropean Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 4
  5. 5. Use of the exponential filterThe recursive formulation of the method relies on (Albergel et al.,2009): * n  X(tn )  Xn1  X  X  Kn  * n1 *  (3)X(tn) surface satellite soil moisture data: SWIX*n profile satellite soil moisture data: SWI*t timetn acquisition time of X(tn)Kn gainT characteristic time length K n 1 Kn   t t  (4)  n n1  K n 1  e  T European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 5
  6. 6. Temporal dynamics of soil moisture andAMSU SWI*Time series of daily rainfall (A). Comparison between the soilmoisture (m3 m−3) simulated by DREAM model and the SWI (K)index as a function of time expressed in days. On y-axes one findsthe SM on the left and SWI on the right side (B). Manfreda et al. (HESS 2011)European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 6
  7. 7. Water flux exchange between the surface and the lower layer The most relevant water mass exchange between the two layers is represented by infiltration. The challenge is to define a soil water balance equation where the infiltration term is not expressed as a function of rainfall, but of the soil moisture content in the surface soil layer. The water flux from the top layer can be considered significant only when the soil moisture exceeds field capacity (Laio, 2006). Assuming thatwhere n1 [-] is the soil porosity of the first layer, Zr1 [L] is the depthof the first layer, s1 (θ1=n1) [-] is the relative saturation of the firstlayer, and sc1 [-] is the value of relative saturation at field capacity. European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 7
  8. 8. Soil water balanceThe soil water balance can be described by the following expression (5)where n2 [-] is the soil porosity, Zr2 [L] is the soil depth, V2 [L/T] isthe soil water loss coecient accounting for both evapotranspirationand percolation losses and s2 [-] is the relative saturation of thesecond soil layer. (6) (7) (8)European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 8
  9. 9. Soil Moisture Analytical Relationship (SMAR)beetween surface and root zone soil moistureAssuming an initial condition for the relative saturation s2(t) equalto zero, one may derive an analytical solution to this lineardifferential equation that is (9)Expanding Eq. 6 and assuming t = (tj - ti), one may derive thefollowing expression for the soil moisture in the second layer basedon the time series of surface soil moisture: (10)European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 9
  10. 10. Soil moisture patterns obtained by hydrologicalsimulationsThe North American Land Data Assimilation System (NLDAS)[Mitchell et al., 2004] provides estimates of soil moisture from fourdifferent models at sub-daily intervals across the United States. TheNLDAS is a multi-institution partnership aimed at developing a real-time and retrospective data set, using available atmospheric andland surface meteorological observations to computethe land surfacehydrological budget.Further informationabout the NLDASproject along withmodel outputs can befound at http://ldas.gsfc.nasa.gov/European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 10
  11. 11. Soil moisture patterns obtained by hydrologicalsimulations Description of the NLDAS domain: DEM, vegetation fraction, soil porosity and topographic index.European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 11
  12. 12. Hydrological Simulation: VIC model Hydrologic Model (Liang et al., 1994)European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 12
  13. 13. SMAR Application to NLDAS data baseComparison between the relative saturation at 10cm and 100cmdepth and the filtered value (S100SMAR - green line) obtained with theSAR (assuming the following parameters: a=0.006, b=0.1,sc1=0.665) and with the exponential filter (assuming T=29) (S100-red line). 1 S10 * S100 RSAR=0.933; RMSE=0.06 S100* 0.9 S100 SAR 0.8 Relative saturation [ ] R=0.971; RMSE=0.09 0.7 0.6 0.5 0.4 0 50 100 150 200 250 300 350 Time [days]European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 13
  14. 14. SMAR Application to NLDAS data baseCorrelation coefficient between the filtered soil moisture and thesimulated soil moisture in the first 100cm at the daily time scale.A) application of theexponential filter (observedmean value of R=0.73).B) application of the SMAR(observed mean value ofR=0.76).C) RMSE obtained with theexponential filter (meanvalue equal 0.45).D) RMSE with SMAR (meanvalue equal 0.10).European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 14
  15. 15. SMAR Application to NLDAS databaseBox-plot of the correlation between S100SMAR and S100 as afunction of rainfall characteristics (rainfall rate  and mean rainfalldepth ).     Correlation [ ]              European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 15
  16. 16. SMAR Application to NLDAS databaseA) Values of correlation between S100SMAR and S100 as a function of the variance of S100. The red open circles describes mean of the observed cloud of data.B) the 2-D histogram of the same data. (A) (B)European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 16
  17. 17. Nature is Complex, butsimple models may suffice. (J. Sprott) Definition of a feasible mathematical characterization of the relationship between the surface soil moisture and the root zone. The model showed high reliability when applied over the conterminous U.S. (plus northern Mexico and southern Canada), mainly in areas characterized by low rainfall. Moreover, the analysis highlighted a significant increase in the performances when the time variability of the soil moisture observed in the deeper layer increases. The skill of the method is therefore encouraging and there is potential to use the method to derive root-zone soil moisture from satellite retrievals.European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 17
  18. 18. Papers related to this research line…Manfreda, S., L. Brocca, T. Moramarco, F. Melone, and J. Sheffield, A physically basedapproach for the estimation of root-zone soil moisture from surface measurements,Hydrology and Earth System Sciences Discussion, 9, 14129-14162, 2012(doi:10.5194/hessd-9-14129-2012).Manfreda, S., T. Lacava, B. Onorati, N. Pergola, M. Di Leo, M. R. Margiotta, and V.Tramutoli, On the use of AMSU-based products for the description of soil water content atbasin scale, Hydrology and Earth System Sciences, 15, 2839-2852, 2011(doi:10.5194/hess-15-2839-2011).Manfreda, S., M. McCabe, E.F. Wood, M. Fiorentino and I. Rodríguez-Iturbe, SpatialPatterns of Soil Moisture from Distributed Modeling, Advances in Water Resources, 30(10),2145-2150, 2007, (doi: 10.1016/j.advwatres.2006.07.009).Manfreda S. and I. Rodrìguez-Iturbe, On the Spatial and Temporal Sampling of SoilMoisture Fields, Water Resources Research, 42, W05409, 2006(doi:10.1029/2005WR004548).Rodríguez-Iturbe I., V. Isham, D.R. Cox, S. Manfreda, A. Porporato, Space-time modeling ofsoil moisture: stochastic rainfall forcing with heterogeneous vegetation, Water ResourcesResearch, 42, W06D05, 2006 (doi:10.1029/2005WR004497). European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 18
  19. 19. Thanks...European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012. 19

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