predicting root zone soil moisture using surface data

1European Geosciences Union, General Assembly 2012, Vienna, Austria, 22 – 27 April 2012.

Predicting root zone soil moisture usingsurface data

Salvatore Manfreda1, Luca Brocca2, Tommaso Moramarco2, Florisa Melone2, Justin Sheffield3, and Mauro Fiorentino1

European Geosciences UnionGeneral Assembly 2012Vienna | Austria | 22 – 27 April 2012

Session: HS6.2: Remote sensing of soil moisture

(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: [email protected]


o The role of soil moisture;

o An analytical relationship between the root zone andsurface soil moisture;

o The study area - NLDAS database;

o Application and validation of the proposed procedure;

o Conclusion.

Predicting root zone soil moisture usingsurface data: Outline


Characterizing 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 patterns

Role of Soil Moisture


Exponential filter (Wagner et al., RSE 1999)

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

The water balance equation

(1)

(2)Ws(t)

W(t)

where T=L/C


Use of the exponential filter

Xn* Xn1

* Kn X(tn)Xn1*

Ttt

n

nn nn

eK

KK1

1

1

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

(3)

The recursive formulation of the method relies on (Albergel et al.,2009):

(4)


Manfreda et al. (HESS 2011)

Temporal dynamics of soil moisture and AMSU 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).


Water flux exchange between the surfaceand the lower layerThe most relevant water mass exchange between the two layers isrepresented by infiltration. The challenge is to define a soil waterbalance equation where the infiltration term is not expressed as afunction of rainfall, but of the soil moisture content in the surfacesoil layer.The water flux from the top layer can be considered significant onlywhen the soil moisture exceeds field capacity (Laio, 2006).Assuming that

where 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.


Soil water balanceThe soil water balance can be described by the following expression

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.

(5)

(6)

(7)

(8)


Soil Moisture Analytical Relationship (SMAR) beetween surface and root zone soil moisture

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:

Assuming an initial condition for the relative saturation s2(t) equalto zero, one may derive an analytical solution to this lineardifferential equation that is

(9)

(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/


Description of the NLDAS domain: DEM, vegetation fraction, soilporosity and topographic index.

Soil moisture patterns obtained by hydrologicalsimulations


Hydrological Simulation: VIC model

Hydrologic Model (Liang et al., 1994)


SMAR Application to NLDAS data base

0 50 100 150 200 250 300 3500.4

0.5

0.6

0.7

0.8

0.9

1

Time [days]

Rel

ativ

e sa

tura

tion

[]

R=0.971; RMSE=0.09

RSAR=0.933; RMSE=0.06

S10S100*

S100S100SAR

*

Comparison 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).


Correlation 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).

SMAR Application to NLDAS data base


Cor

rela

tion

[]

Box-plot of the correlation between S100SMAR and S100 as afunction of rainfall characteristics (rainfall rate and mean rainfalldepth ).

SMAR Application to NLDAS database


A) Values of correlation between S100SMAR and S100 as a function ofthe variance of S100. The red open circles describes mean of theobserved cloud of data.

B) the 2-D histogram of the same data.

(A) (B)

SMAR Application to NLDAS database


Nature is Complex, butsimple models may suffice. (J. Sprott)

Definition of a feasible mathematical characterization of therelationship between the surface soil moisture and the root zone.

The model showed high reliability when applied over theconterminous U.S. (plus northern Mexico and southern Canada),mainly in areas characterized by low rainfall.

Moreover, the analysis highlighted a significant increase in theperformances when the time variability of the soil moistureobserved in the deeper layer increases.

The skill of the method is therefore encouraging and there ispotential to use the method to derive root-zone soil moisture fromsatellite retrievals.


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).


Thanks...

predicting root zone soil moisture using surface data

Education