comparative assessment of soil moisture estimation from land surface model and satellite remote...

METEOROLOGICAL APPLICATIONSMeteorol. Appl. (2013)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/met.1357

Comparative assessment of soil moisture estimation from land surface modeland satellite remote sensing based on catchment water balance

Deleen Al-Shrafany,a* Miguel A. Rico-Ramirez,a Dawei Hana and Michaela Brayb

a Department of Civil Engineering, University of Bristol, UKb School of Engineering, University of Cardiff, UK

ABSTRACT: Land surface models and satellite remote sensing are among the modern soil moisture retrieval techniquesthat can be used over large areas. However, the lack of ‘ground truth’ soil moisture measurements is still an obstaclein the evaluation and validation of soil moisture retrievals. In this study, a new scheme is used to assess soil moistureretrieval from both the NOAH Land surface Model (LSM) coupled with the fifth generation Mesoscale Model MM5and the Advanced Microwave Scanning Radiometer AMSR-E. The proposed scheme is based on the strong correlationbetween changes in soil storage from rainfall runoff events and changes in the retrieved soil moisture either from theMM5-NOAH LSM or the AMSR-E. The aim of this study is to compare the application of the proposed scheme betweenthese two different approaches for soil moisture estimation. The MM5-NOAH LSM provides soil moisture estimations atthree different layers with depths 0–10 cm (surface layer 1), 10–40 cm (layer 2) and 40–100 cm (layer 3). In this study,the combined soil moisture over the top two layers (first and second) and the combined soil moisture over the first threelayers (first, second and third) are used to account for the entire soil column for assessing the estimated soil moisture usingchanges in the storage from the water balance. The results have shown that the MM5 soil moisture from the combined toptwo layers has the better performance than either of the individual layers when compared to the catchment water storage.The results also show that the MM5-NOAH LSM soil moisture estimates have a slightly better performance than theAMSR-E surface soil moisture measurement. This preliminary assessment shows the benefits of using hydrological datain the validation of soil moisture retrieval methods. Copyright 2013 Royal Meteorological Society

KEY WORDS soil moisture retrieval; MM5-NOAH LSM; water balance; AMSR-E

Received 6 January 2012; Revised 9 July 2012; Accepted 17 July 2012

1. Introduction

Soil moisture is one of the most important variables thatintegrates much of the land surface hydrology through thewater and energy exchanges at the land–atmosphere interface.It affects surface temperature, the depth of the planetaryboundary layer (PBL), circulation-wind patterns, and regionalwater energy budgets (Mahfouf et al., 1987; Lakshmi et al.,1997). Surface soil moisture plays a crucial role in hydrologyand meteorology as it is important to estimate the ratio betweenevaporation and potential evaporation over the land surface, toestimate the distribution of precipitation between runoff andstorage, and to computing several key variables of the landsurface energy and water budget such as albedo and hydraulicconductivity (Wigneron et al., 2003). Regionally, soil moisturehas a controlling function in the hydrological cycle in generalthrough the interaction with the atmosphere and influences theclimate. On the other hand, at the field scale soil moisturesignificantly affects the generation of runoff and erosion, plantgrowth and other important disciplines in agricultural andenvironmental fields (Lakshmi et al., 1997).

Soil moisture is a highly variable parameter both spatiallyand temporally due to the heterogeneity of soil properties,topography, land cover, evapotranspiration and precipitation.

∗ Correspondence to: D. Al-Shrafany, Department of Civil Engineer-ing, University of Bristol, Bristol BS8 1TR, UK.E-mail: [email protected]

As a result, soil moisture is often somewhat difficult to mea-sure accurately in both space and time, especially at largescales (Engman, 1991; Owe et al., 2001). Ground measure-ments, remote sensing and land surface models are the mainsources that could provide soil moisture information. Con-ventional field measurement techniques have serious limita-tions in their ability to estimate the spatial distribution of soilmoisture appropriately. This is because they are point-basedmeasurements and cannot represent the spatial distribution ofthe soil moisture particularly over large areas where denseground networks are needed. However, a significant corre-lation between the in situ soil moisture observation and thecoarse resolution product of soil moisture has been observedin previous studies (Albergel et al., 2010, 2012; Brocca et al.,2010). Remote sensing techniques provide the most feasiblecapability to monitor soil moisture over a range of spatialand temporal scales (Jackson and Schmugge, 1989). Variousremote sensing techniques have been evaluated and proven tobe a valuable source of information for the measurement ofsoil moisture. For the past two decades, microwave remotesensing radiometry has been extensively used for soil moisturemonitoring over different conditions of topography and vegeta-tion. Moreover, passive microwave radiometric measurementsin the 1–6 GHz range have been very valuable in estimat-ing soil moisture (Calvet et al., 2011). However, remote sens-ing can observe the spatial distribution of soil moisture onlyin the top few centimetres of soil surface (Schmugge et al.,1983).

Copyright 2013 Royal Meteorological Society

D. Al-Shrafany et al.

The use of land surface models (LSMs) assimilated withmeteorological observations is another approach to estimate soilmoisture at regional or global scales through the integration ofthe atmospheric forcing with the physical formulation of theLSM (Gao et al., 2004). Energy and water exchange occurscontinuously at the interface between the land surface and thelower atmosphere. Thus, a strong connection exists betweenthe land surface hydrological processes and the weather. Inaddition, the conversion of thermal and radiative energy tolatent heat is responsible for the linkage between the energyand water balances. Hence, it has been widely accepted thatland surface processes play a vital role in the mesoscaleatmospheric models that resolve spatial scales from 1 to 100 km(Chen and Dudhia, 2001; Sridhar et al., 2002). As a result,a number of LSMs have been developed in recent years fortheir application in mesoscale meteorological models in whichmesoscale weather is significantly affected by the local variationof temperature and moisture. Increasingly finer spatial andtemporal resolutions and improved planetary boundary layerPBL parameterization were the key motivation behind thatprogress (Chen and Dudhia, 2001). The role of soil moisturein regional weather prediction and the interaction between theatmosphere and the land surface have been demonstrated inmany studies (Hipps et al., 1994; Betts et al., 1996; Entekhabiet al., 1996; Chen and Dudhia, 2001; Sridhar et al., 2002;Sahoo et al., 2008; Chen et al., 2010; Patil et al., 2011).It has been shown that physically-based modelling is animportant tool for studying the coupling between the LSMand the mesoscale atmospheric model in order to representthe temporal evolution of soil moisture (Reen et al., 2006).LSMs such as the one developed by the National Centrefor Environmental Prediction (NCEP)/Oregon State University(OSU)/Air Force/Office of Hydrology (NOAH) (Pan and Mahrt,1987; Chen et al., 1996) have been coupled with the fifthgeneration Mesoscale Model MM5.

Most of previous LSM research (either coupled or uncou-pled with a meteorological weather model) has been focusedon either improving or evaluating the model performance (Liuet al., 2005; Miao et al., 2007; Koster et al., 2009; Van dervelde et al., 2009). However, the relevance of surface soilmoisture to flood prediction and to a range of agricultural appli-cations was the reason behind a published study by Kong et al.(2011) where the UK Met Office Surface Exchanges Scheme(MOSES) was used to represent land surface processes in theMet Office’s Unified Model (MetUM) for soil moisture esti-mation over an agricultural site in Norfolk. The validationresults of MOSES versus ground soil moisture measurementsshowed that MOSES performs well in soil moisture estima-tion in general. Another numerical investigation was conductedby Hossain et al. (2005) aimed at evaluating the uncertaintyin the prediction of soil moisture from a one dimensionaluncoupled land surface model forced by hydro-meteorologicaland radiation data. A study conducted by Venalainen et al.(2005) concluded that the potential evaporation calculated usingthe Penman–Monteith equation could be estimated accuratelyusing data obtained from the output of a high resolution numeri-cal atmospheric model (HIRLM, High Resolution Limited AreaModel).

It is obvious that there is a lack of research in the eval-uation of soil moisture retrievals from coupled LSMs withmesoscale models. Hence, the NOAH LSM coupled with thenumerical weather measoscale model MM5 is adopted in this

study to estimate and examine the estimation of soil mois-ture. The NOAH Land surface model was developed by sev-eral groups in the US (NCEP, Oregon State University, theAir Force Weather Agency and Air Force Research Lab andthe Office of Hydrology Division: see http://www.emc.ncep.noaa.gov/mmb/gcp/noahlsm/Noah LSM USERGUIDE 2.7.1.htm).

However, evaluating and assessing the performance of suchan LSM is not a trivial exercise due to the lack of high-resolution spatial and temporal ground measurements of soilmoisture. Recently, the International Soil Moisture NetworkISMN was initiated to overcome many of these limitations (seeDorigo et al., 2011), as the network participants share their soilmoisture datasets with the ISMN on a voluntary and no-costbasis. Nevertheless, their data coverage is still very limited. Inthe UK, only limited data sets of soil moisture measurementsexist while at the same time there is an abundance of rainfalland flow measurements. Therefore, from a hydrological pointof view, a new scheme is proposed to assess the soil moistureretrieval from satellite (Al-Shrafany et al., 2012), which isfurther evaluated in this study. This scheme is used to assessand validate the estimated soil moisture values from the threesoil layers of the MM5-NOAH LSM and moreover to comparethe soil moisture estimations from the MM5-LSM and satellite.The proposed scheme is an empirical approach based on thecorrelation between changes in the catchment water storagecalculated from the water balance equation and changes in thesoil moisture retrieved using the NOAH LSM coupled withthe MM5. The results from this approach (as compared within situ soil moisture sensors) are more relevant to hydrologicalapplications.

The key points presented in this paper concern researchfirstly to retrieve soil moisture using the NOAH LSM coupledwith the MM5 over three different depths of soil profile,and secondly to assess the performance of the soil moistureestimations over the different soil depth combinations. Anintercomparison between the LSM soil moisture and satellitesoil moisture is also shown at the end of this paper.

2. Study area and data

The Brue catchment was selected to be the study area due tothe availability of hydrological data over a continuous longperiod and because it was considered to be a representative ruralUK catchment used for hydrological modelling. It is locatedin Somerset, South West England (51.075 °N, 2.58 °W) witha drainage area of 135 km2 and its terrain elevation rangesbetween 115 and 190 m (Figure 1). The Brue catchment ispredominantly a rural catchment of modest relief as most ofthe catchment is agricultural land (95.22%) with a few patchesof forest (3.12%) and residential areas (1.66%). In terms of soiltexture, 49% of the catchment is clay, 29% is fine loam and 21%is silt (Mellor et al., 2000). In this study, the UK EnvironmentAgency provided hourly catchment-averaged rainfall and riverflow data for the Brue catchment for the years 2004–2006.

The European Centre for Medium-Range Weather Forecasts(ECMWF) has in the past produced three major re-analyses datasets: FGGE, ERA-15 and ERA-40. The last of these consistedof a set of global analyses describing the state of the atmosphereand land and ocean-wave conditions from mid-1957 to mid-2002. The ERA-Interim as initially described by the ECMWF(Dee et al., 2011) is an ‘interim’ re-analysis for the period1979-present in preparation for the next-generation extended

Copyright 2013 Royal Meteorological Society Meteorol. Appl. (2013)

Land surface model coupled to numerical weather model

Figure 1. Brue catchment site map and digital terrain elevation inUK National Grid Reference (the black dot symbol represents the

catchment).

re-analysis to replace ERA-40 (see ECMWF Newsletters No.111 and 115). The ERA-Interim products are publicly availableon the ECMWF Data Server, at a 1.5° resolution and canbe accessed by the ECMWF’s Meteorological Archive andRetrieval System (MARS). The ERA-Interim archive is moreextensive than that for ERA-40, e.g. the number of pressurelevels is increased from ERA-40 23–37 levels and additionalcloud parameters are included. In this study, the years 2004 and2005 of the ERA-Interim re-analysis data with 6 h temporalresolution were used as initial and lateral boundary conditionsto drive the Numerical Weather Prediction (NWP) model MM5.

Microwave brightness temperature measurements at6.9 GHz for the same years (2004–2006) from the AMSR-E on board the Aqua satellite were used for satellite soilmoisture estimation. AMSR-E measures brightness temperatureat six frequencies (6.9, 10.7, 18.7, 23.8, 36.5, and 89 GHz)with both horizontal (H) and vertical (V) polarizations. Moredetailed information about the AMSR-E data used in this studyis available in Al-Shrafany et al. (2012) and other related gen-eral information can be found in the technical paper (Knowleset al., 2006) published by the National Snow and Ice DataCentre (NSIDC). The years used for calibration of the phys-ical models were 2004 and 2005, whereas the validation wasperformed in the year 2006.

3. Methodologies

3.1. MM5-NOAH land surface model configuration

The Fifth-Generation NCAR/Penn State Mesoscale ModelMM5 is the latest in a series developed from a mesoscalemodel used at Penn State in the early 1970s that was laterdocumented by Anthes and Warner (1978). Particular attentionhas been given to the non-hydrostatic dynamics, multi nestingcapability and four dimensional data assimilation. Althoughmesoscale model users are being encouraged to move on tothe next generation mesoscale model, the Weather Researchand Forecasting system (WRF) (Skamarock et al., 2005), theMM5 system is still a popular choice due in part to itsperformance, availability and stability (see for instance Liguoriet al., 2009, 2011). Mesoscale models in general and MM5 inparticular are often used for three key applications: (1) regional

climate simulations, (2) numerical weather prediction, and,(3) air quality prediction. However, the MM5 can be coupledwith a Land surface model, and one of the main prognosticparameters is the volumetric soil moisture vsm. The MM5is a grid based model, using the finite differencing methodto resolve the model dynamics at different pressure levels.Detailed information of the MM5 dynamics and its integrationcan be found in the MM5 online tutorial by Dudhia et al.(2005).

The original LSM was developed at the Oregon StateUniversity (OSU) by Pan and Mahrt (1987). It is based onthe coupling of the diurnally dependent Penman potentialevaporation approach of Mahrt and Ek (1984), the multilayersoil model of Mahrt and Pan (1984), and the primitive canopymodel of Pan and Mahrt (1987). It has been modified byChen et al. (1996) to include an explicit canopy resistanceformulation used by Jacquemin and Noilhan (1990) and asurface runoff scheme of Schaake et al. (1996). The NOAHLSM has benefited from a series of improvements particularlyin increasing the soil layers from two to four. Hence, it is widelyadopted by the National Centres for Environmental Prediction(NCEP) and showed an adequate performance in the NCEPcoupled Eta Model. This is one of the reasons why the NOAHLSM was selected to be implemented in the MM5 modelbesides its moderate complexity (Chen and Dudhia, 2001). Thecoupled MM5-NOAH LSM model has a vertical soil profilewith a total depth of 2 m below the surface and it is partitionedinto four soil layers with lower boundaries at 10, 40, 100 and200 cm below the surface (Figure 2). The root zone is fixedat 100 cm (i.e. including the top three soil layers). Thus, thelower 100 cm of soil layer acts as a reservoir with gravitydrainage at the bottom. The MM5-NOAH LSM has one canopylayer and one snow layer and has the following prognosticvariables: soil moisture and soil temperature in the soil layers,canopy moisture, snow height, and surface and ground runoffaccumulation. Evapotranspiration is handled by using soil andvegetation types. The vegetation characteristics of each grid ofthe model are represented by the dominant vegetation type ofthat grid because the model horizontal grid resolution is largerthan 1 km × 1 km.

The soil thermal properties depend on the soil type. The soilwater movement and flow between the soil layers is governedby the mass conservation law and the diffusivity form ofRichards’ equation (Chen and Dudhia, 2001) as follows:

∂θ

∂t= ∂

∂z

(D

∂θ

∂z

)+ ∂K

∂z+ Fθ (1)

where θ is the volumetric soil water content, D is the soilwater diffusivity (m2 s−1) and K is the hydraulic conductivity(m s−1) and both are functions of θ ; t is time (s) and z is thesoil layer depth (m); and Fθ is represent sources and sinks forsoil water (i.e., precipitation, evaporation and runoff). D and K

are highly nonlinearly dependent on the soil moisture (Chen andDudhia, 2001) and in particular when the soil is dry, they canchange several orders of magnitude even for a small variationin soil moisture. By expanding and integrating Equation (1)(Chen and Dudhia, 2001) over four soil layers, the followinglayers are produced:

dz1∂θ1

∂t= −D

(∂θ

∂z

)z1

− Kz1 + Pd − R − Edir − Et1 (2)

dz2∂θ2

∂t= D

(∂θ

∂z

)z1

− D

(∂θ

∂z

)z2

+ Kz1 − Kz2 − Et2 (3)



Figure 2. Vertical soil profile layers of the MM5-NOAH LSM.This figure is available in colour online at wileyonlinelibrary.com/

journal/met

dz3∂θ3

∂t= D

(∂θ

∂z

)z2

− D

(∂θ

∂z

)z3

+ Kz2 − Kz3 − Et3 (4)

dz4∂θ4

∂t= D

(∂θ

∂z

)z3

+ Kz3 − Kz4 (5)

where, dz(1 : 4) is the soil layer thickness (for layers 1–4respectively); Pd is the precipitation not intercepted by thecanopy; Et(1 : 3) is the canopy transpiration taken by the canopyroot within the root zone layers (the root zone is up tothree layers in the coupled MM5-LSM); and Edir is the directevaporation from the top surface soil layer. A conceptualparameterization for the sub-grid treatment of precipitation andsoil moisture is governed by the infiltration. The heat transferthrough the soil vertical profile is governed by the thermaldiffusion equation. A single linearized surface energy budgetequation is applied to determine the surface temperature toreflect a linearly combined ground-vegetation surface (Chenand Dudhia, 2001; Chen et al., 2010). The surface runoff iscalculated using the Simple Water Balance (SWB) technique.A detailed description of the MM5-NOAH LSM can be foundin (Chen and Dudhia, 2001).

For the purposes of this study, the MM5 model was set up tohave three domains (D1, D2 and D3) with grid resolutions of108 km for the outside domain, 36 km for the middle domainand 12 km for the inner domain. The innermost domain D3is able to capture the local scale features of the study area.The MM5 domains are nested with two-way interaction, inwhich the boundary conditions for the finer grid are generatedfrom the coarse grid model results while the fine grid modelresults update the variables on the coarse grid (Dudhia et al.,2005/ MM5 tutorial). In addition, the standard nesting ratioused by MM5 in every time step is 3 : 1, in which each domaintakes information from the mother domain. For each motherdomain time step, the domain runs three time steps beforefeeding back information to the mother domain. Hence, nesteddomains feeding back to each other can lead to improvedmodel behaviour at the boundaries. In order to mitigate thespatial distortion associated with the map projection applied,the domains are positioned in such a way that the Bruecatchment is located at the centre of all three domains (seeFigure 3).

10 °W 5 °W

5 °W

5 °E

55 °N

50 °N

55 °N

50 °N

GM

GM

Figure 3. Configuration of the MM5-NOAH LSM domains D2 andD3 to produce a 12 km resolution (D3) soil moisture over the Brue.This figure is available in colour online at wileyonlinelibrary.com/

journal/met

The model is configured to have 23 vertical levels withthinner layers near the ground surface (the model top is at100 hPa) and a dimensionless quantity σ is used to define themodel’s vertical levels as it is shown in the following equation:

σ = (p0 − pt)

(ps0 − pt)(6)

where p0 is the reference-state pressure, pt is a specifiedconstant top pressure, and ps0 is the reference-state surfacepressure.

Four Dimensional Data Assimilation (FDDA) is used inwhich a full-physics of the model is run while incorporatingobservations/re-analysis. In this way a dynamical consistencywould be assured while the observations keep the model closeto the true conditions (Stauffer and Seaman, 1990). Newtonian-relaxation or nudging is the technique used by the MM5 modelin such data assimilation scheme. In this technique, an artificialtendency term based on the difference between the model stateand the observed state is added to the prognostic equations for aparticular variable such as wind, temperature and water vapourin order to relax the model state towards the observed state or agiven analysis. For this study, the grid nudging is implementedthrough feeding the model in its standard input format with thegiven re-analysis on the model grid over the data assimilationperiod. Thus, the model relaxes its solution towards the re-analysis data. The Nudging Factor Gα (where α represent aparticular variable) determines the relative magnitude of theterm that is added into the variable prognostic equation. Inthis study, three dimensional analysis nudging is implementedfor the wind and temperatures fields and their nudging factorvalues are selected according to the study done by Stauffer andSeaman (1990).

The cumulus parameterization scheme used in this study forthe MM5 model was the Kain–Fritsch (KF), which is based onrelaxation to a profile and predicts both updraft and downdraftproperties and also detrains cloud and precipitation. Thiscumulus parameterization scheme uses a sophisticated cloud-mixing scheme in order to determine entrainment/detrainment.More details can be found in Kain and Fritsch (1993). For thePlanetary Boundary Layer PBL, the MRF or Hong-Pan schemewas the selected option due to its suitability for high resolution



in PBL. This scheme was implemented in the NCEP MRFmodel (see Hong and Pan, 1996 for more details). Mixed-Phasewas the selected scheme that dealt with the microphysics of themodel. This scheme adds supercooled water to cloud and rainwater field that predicted explicitly the microphysical processes.The NOAH LSM was used to retrieve the surface parametersand in particular soil moisture (see Chen and Dudhia, 2001).

3.2. MM5-NOAH LSM soil moisture estimation

Numerical experiments were conducted in this study for severalevents in 2004–2006 to predict a continuous time series ofthe soil moisture for three soil layers. The ECMWF/Era-interim/reanalysis data with a spatial resolution of 1.5° × 1.5°

and a temporal resolution of 6 h was used in this study asinitial and lateral boundary conditions. The boundary conditionsare fed on to the coarsest domain which has a comparableresolution (108 km) and it is then dynamically downscaled allthe way down, from 108 km for domain 1–36 km and 12 kmfor domains 2 and 3 respectively. The smallest resolution of12 km is consistent with the area covered by the Brue catchmentarea. The classification of vegetation by the U.S GeologicalSurvey (USGS) is adopted in the MM5-NOAH LSM to definethe vegetation types that cover the study area, whereas the soiltypes are defined by the Food and Agriculture Organisation(FAO) database. Default values of the model parameters suchas the soil and vegetation parameters were selected in this study.All simulations are conducted from 0000 UTC 1 January 2004to 0000 UTC 31 December 2004 for the first simulation andsimilarly for the follow-on simulations using 2005 and 2006data. The model output was retrieved at hourly intervals. Asa result, 3 year hourly time series of three soil layers (withlayer thicknesses of 10, 30, 60 cm) of soil moisture valuesare estimated from the three MM5-NOAH LSM domains. Theinnermost domain (domain 3) soil moisture was adopted inthis study (see Figure 4(c)), as domain 3 has the highest andrequired spatial resolution of 12 km, which has a similar areato the Brue catchment.

A three-level soil layer configuration was adopted in thecoupled MM5-NOAH LSM in this study in order to capturethe daily, weekly and seasonal evolution of soil moistureand mitigating the possible truncation error in discretization(Sridhar et al., 2002). The combination of soil moisture overseveral layers was adopted here to take into account the entiredepth of the soil column when using the water balance changingin the storage to assess the estimated soil moisture from theNOAH LSM. It is assumed that the soil is homogenous andhas no significant variations in its characteristics. Hence, twocases of layer combinations were produced. Firstly, the soilmoisture was combined over the first two layers (first andsecond) of the NOAH LSM where the soil column depth wouldbe 40 cm. Secondly, the soil moisture over the three first layers(first, second and third) was combined with a total soil columndepth of 100 cm. The soil moisture in the combined layers iscomputed by:

θ(1+2) = (θ1z1) + (θ2z2)

z1 + z2(7)

θ(1+2+3) = (θ1z1) + (θ2z2) + (θ3z3)

z1 + z2 + z3(8)

where θ is the original soil moisture estimation from the LSM,z is the soil layer depth, and the subscripts (1, 2 or 3) indicatethe soil layer indices.

For comparison purposes of the MM5-NOAH LSM soilmoisture assessment against the retrieved soil moisture fromsatellite, a third set of numerical experiments was conductedfor some selected events in 2006. The same simulation settingswere used for the 2004, 2005 and 2006 simulation experiments.A statistical t-test was conducted to examine the significance ofthe linear correlation between the changing in the water storageand the changes in the MM5-NOAH LSM soil moisture. In sucha test, the p-value, which measures the strength of evidence,is computed. A value of 0.05 was adopted as the significancelevel. If the computed p-value is below the significance levelthen there is a significant linear relationship between the changein the storage and change in the estimated soil moisturefrom the MM5-NOAH LSM, otherwise there is no statisticalsignificance. The t-test results are presented later in this paper.

3.3. AMSR-E soil moisture estimation

The explanation of estimation of near surface soil moisture fromthe AMSR-E satellite is summarized in this section. It is impor-tant to mention that a detailed description of the developedmethodology to estimate soil moisture from satellite can befound in Al-Shrafany et al. (2012). The AMSR-E instrumentis a passive microwave radiometer that makes measurementsof thermal radiation from the land surface in the centimetrewave band at ascending (1300) and descending (0130) passes(Owe et al., 2001; Njoku et al., 2003). The AMSR-E measure-ments from the descending pass were considered in this study.This is due to the reasonable uniform temperature and mois-ture profiles at night times, while in the daytime soil moistureestimates may reflect the effects of diurnal surface layer dry-ing. The techniques adopted for soil moisture retrieval providedspatially averaged soil moisture data, which is ideal for envi-ronmental and hydrological modelling and monitoring. Suchspatially averaged area data sets are logistically and economi-cally difficult to obtain through traditional in situ measurementtechniques. The technique only uses the horizontal and verti-cal polarization brightness temperatures Tb at one frequency(6.9 GHz) observed by the AMSR-E in a descending mode.The use of lower frequencies (e.g. 6.9 GHz) allows a greaterpenetration depth and the measurements are less affected by thevegetation (Schmugge et al., 2002).

The AMSR-E satellite has a footprint size of 25 km at whichall retrieval calculations are based on. With respect to the aver-age soil and vegetation biophysical characteristics, a uniformfootprint is assumed. Therefore, the surface soil moisture andvegetation optical depth are subsequently extracted as aver-aged footprint values. The soil and vegetation temperatures areassumed to be approximately equal in the use of the AMSR-Edescending measurements as the temperature and soil profilesare reasonably uniform. Moreover, the effects of the atmo-spheric moisture and the multiple scattering in the vegetationlayer are negligible due to the AMSR-E low frequency mea-surements (up to X-band, i.e. ∼10 GHz). The radiative transferequation explains the relationship between surface parameterssuch as surface soil moisture, vegetation water content, surfacetemperature, and microwave brightness temperature (Tb) (Jack-son et al., 1982; Njoku et al., 2003). It includes contributionsfrom the soil, vegetation and atmosphere in the upwelling radi-ation from the land surface as observed by the instrument. Thebrightness temperatures at H and V polarizations are given by:

TbH = Ts{esH�H + (1 − ω)(1 − �H)[1 + (1 − esH)�H]} (9)

TbV = Ts{esV�V + (1 − ω)(1 − �V)[1 + (1 − esV)�V]}(10)



Figure 4. Brue catchment (2004–2005): (a) evapotranspiration (ET), (b) precipitation (c) volumetric soil moisture (VSM) from the MM5 LSMfor three single layers with soil depths: surface layer (0–10 cm), second layer (10–40 cm) and third layer (40–100 cm); and (d) VSMfrom the MM5 LSM combining several layers; (e) AMSRE satellite soil moisture time series. This figure is available in colour online at

wileyonlinelibrary.com/journal/met

where the subscripts H and V refer to the horizontal andvertical polarizations respectively; Ts is the single surfacetemperature; es is the soil emissivity at H (esH) and V (esV)polarizations; ω is the vegetation single scattering albedoand � is the transmissivity. The contributions from soil andvegetation represented by the surface roughness and densityof vegetation canopy respectively have significant effects onthe soil reflectivity. Surface roughness reduces the sensitivityof emissivity to soil moisture variations, and thus reduces therange in measurable emissivity from dry to wet soil conditions(Wang, 1983). The statistical parameters that characterize thescale of roughness of a randomly rough surface are knownas the h and Q parameters. The h-Q model developed byWang and Choudhury (1980) was considered in Al-Shrafanyet al. (2012) to account for the roughness effects when the soilmoisture was retrieved from the AMSR-E.

The vegetation will absorb or scatter the radiation emanatingfrom the soil, and it will also emit its own radiation. Gener-ally speaking, the integral contribution of the surface roughnessand vegetation canopy is more difficult to separate unless oneof them is known a priori. An analytical approach developed byMeesters et al. (2005) is considered in Al-Shrafany et al. (2012)

for calculating vegetation optical depth from the MicrowavePolarization Difference Index (MPDI) and the dielectric con-stant of the soil. The MPDI effectively normalizes out theeffects of the surface temperature, resulting in a quantity thatis highly dependent on the soil moisture and vegetation. TheMPDI is defined as:

MPDI = TbV − TbH

TbV + TbH(11)

Hence, the brightness temperatures are converted to volumetricsoil moisture values with the Land Parameter Retrieval ModelLPRM (Owe et al., 2008; Wang et al., 2009). The volumetricsoil moisture vsm is retrieved from Equation (11) (after substi-tuting in Equations (9) and (10) and taking into account of theroughness effect) as:

MPDI = esV − esH

esV + esH(1 − 2Q)

{1 + 2

esV + esH[exp(2τ + h) − 1]

}−1

(12)



where esH and esV is the soil emissivity at H and V polarizationrespectively, τ is the vegetation optical depth. The h and Q

parameters are empirically calibrated since the lowest frequencyof the AMSR-E instrument is at 6.9 GHz, and its footprintscale is large which results in no data available to quantifythe regional variability of the those parameters. Therefore,in order to estimate the optimal (h and Q) values for aparticular catchment area, a new approach is proposed in Al-Shrafany et al. (2012) for this purpose. That approach usedthe event-based water balance approach in the context ofcatchment storage calculation. Hence, for a range of h andQ values, the volumetric soil moisture was retrieved from theAMSR-E using Equation (12) and taking into account the bestcorrelation between changes in the water storage �s (from thewater balance using rainfall, runoff and evapotranspiration) andchanges in the satellite soil moisture �θ . The optimal values forthe h and Q parameters were obtained when the best correlationbetween �s and �θ was achieved. Al-Shrafany et al. (2012)also showed that changes in the volumetric soil moisture werevery sensitive to the selection of the h parameter, but lesssensitive to the selection of the Q parameter. This methodwas considered as a potential technique to assess the retrievedsoil moisture from the AMSR-E satellite for hydrologicalapplications. In this current study, the same approach is alsoadopted to assess the estimated soil moisture from the threelayers of the MM5-NOAH LSM. Therefore, a brief summaryof the event-based water balance approach is presented in thenext section.

3.4. Water balance as a proposed scheme

Water balance is a modelling framework for simplifying,describing and quantifying the hydrological water budget. Itcan be applied to a catchment area within a time interval(annual, monthly, weekly). Water balance is hydrologicallydriven by the variation in precipitation and temperature, besidessome other local factors such as vegetation, soil and land use.A water balance can be used to help manage water supplyand predict where there may be water shortages. It is alsoused in irrigation, runoff assessment (e.g. through the Rainfall-Runoff model), flood control and pollution control. In thisstudy, the water balance is the basis of the proposed schemeand offers a preliminary validation tool in the hydrological andmeteorological community for the retrieved soil moisture fromthe MM5-NOAH LSM. This scheme was developed due to thelack of ground in situ measurements of soil moisture in theUK and most other places around the world. The scheme canbe used to calibrate and validate soil moisture estimations inlarge areas due to the abundance of hydrological data (rainfalland flow) such as the Brue catchment. The performance ofthis scheme is based on the correlation between the changein catchment water storage �s and the corresponding changein soil moisture �θ retrieved from the MM5-NOAH LSMapproach calculated on an event basis.

The water balance equation is given by:

P = R + ETo + �s (13)

where P is the precipitation in mm, R is the runoff volumein mm, ET 0 is the evapotranspiration in mm and �s isthe change in soil water storage in mm. The water balancetakes into account the main hydrological processes takingplace within the catchment. The evapotranspiration (ET0) iscalculated with a method called the Penman Monteith equation

recommended by the Food and Agriculture Organisation (FAO)(Allen et al., 1998) and it is used in this study to incorporatethe impacts of the evapotranspiration in the water storagecalculations. The FAO Penman–Monteith method requires thefollowing meteorological data: solar radiation, air temperature,air humidity and wind speed, to derive the parameters forcalculating ET 0. The observed meteorological data for the Bruecatchment were obtained from the British Atmospheric DataCentre BADC. The mathematical formulation of the Penman-Monteith equation and all the related calculation procedures canbe found in the FAO report published by Allen et al. (1998).

The water balance application in this study is an event-basedapproach. The rainfall-runoff events have been chosen over a3 year period (2004–2006) and for each selected event, thetotal runoff volume (both direct runoff and base flow) can becalculated using the measured flows. Several selected rainfall-flow events were used to assess soil moisture estimation fromthe MM5-NOAH LSM (see Tables 1 and 2). The change in thesoil moisture �θ is given by:

�θ = θ2 − θ1 (14)

where �θ is the change in the vsm, θ1 is the vsm before theevent and θ2 is the vsm after the event. Assuming a sufficientnumber of rainfall-runoff events, the correlation between �sand �θ can be calculated. The following section explains indetail the result of this analysis.

4. Results and discussion

4.1. MM5-NOAH LSM soil moisture results

Numerical experiments were conducted for several events in2004–2006 (2 years of data were used to assess the MM5-NOAH LSM scheme and 1 year in 2006 was used to comparewith the satellite soil moisture retrieval). As a result, a 3 yeartime series of soil moisture values were estimated from thethree MM5 domains. The soil moisture results produced fromdomain 3 were adopted in this study as presented in Figure 4.It can be seen from this figure that for all the three layers,the soil moisture predictions are consistent with the observedrainfall and evapotranspiration dynamics. Furthermore, thesurface layer (0–10 cm) soil moisture is more variable incomparison to the other two deeper layers as it rapidly respondsto changes in rainfall and evapotranspiration. The results showthat 2004 had the ‘wetter summer’ compared with 2005 as ithad a total rainfall over the summer (June, July and August)of around 311.8 mm, while for 2005 the total rainfall for thesame period was 167.2 mm. There is no significant increase inthe evapotranspiration measurements over that year except forsome sporadic cases. Thus, relatively high soil moisture valuesare shown in late summer and autumn. In addition, the resultsfor the year 2004 also show the typical climatological annualcycle of soil moisture.

Spatial, temporal and vertical resolutions are important issuesthat need careful consideration when comparing soil moistureestimations from different sources for assessment and vali-dation purposes. Field measurements are usually obtained atpoint scales whereas the MM5-NOAH LSM simulates theland surface processes averaged over 12 km spatial grids.From the aforementioned water balance scheme, event-based�s calculation was adopted here, where 33 rainfall-runoffevents were selected over a period of 2 years 2004–2005 (see



Table 1. Results summary of �s (change in the storage) and �θ (changes in the MM5-NOAH LSM soil moisture estimation) over three soillayer depths for the Brue catchment in (2004 and 2005).

Flowevent

Duration Totalrain

Run offvolume

TotalET

�S

(mm)�θ (%) at single

LSM layers�θ (%) at combined

LSM layers(mm) (mm) (mm)

2004 Start date, time End date, time (0–10)cm

(10–40)cm

(40–100)cm

(0–40)cm

(0–100)cm

January 1 6 January 0200 11 January 0000 27.8 16.6 4.2 7.0 0.75 0.75 1.50 1.30 1.65January 2 11 January 0100 13 January 0100 28.4 16.1 4.2 8.1 1.00 1.00 0.75 0.67 0.93February 1 1 February 0200 3 February 0100 14.7 11.2 3.5 0.03 0.00 0.00 0.00 0.13 0.13February 2 6 February 0200 8 February 0200 31.9 20.5 4.4 7.0 0.50 0.75 1.75 0.57 0.98March 1 11 March 0100 18 March 0200 22.2 4.5 4.2 13.5 1.25 2.00 1.75 1.65 1.60March 2 18 March 0300 23 March 0200 14.6 4.9 7.4 2.3 0.75 1.25 1.75 0.96 1.15April 21 April 0200 24 August 0200 13.8 5.2 7.4 1.2 0.25 0.25 0.75 0.21 0.60May 4 May 0100 8 May 0200 34.8 11.8 10.3 12.7 1.50 1.25 0.50 1.01 0.85June 22 June 0200 26 June 0000 33.4 1.4 15.7 16.3 1.50 2.00 0.75 1.52 1.38July 7 July 0100 13 July 0200 37.4 3.7 19.4 14.3 2.25 2.00 2.25 1.72 2.48August 2 August 0100 6 August 0000 25.5 0.9 14.8 9.8 0.25 0.50 0.00 0.62 0.27September 17 September

230021 September0000

4.8 0.8 10.1 −6.1 0.00 0.00 0.75 −0.45 0.82

October 1 2 October 0200 6 October 2300 31.2 4.4 8.1 18.7 1.00 2.00 1.25 1.68 1.52October 2 13 October 0100 16 October 0000 11.5 4.9 4.7 1.9 0.50 0.75 1.25 0.61 1.02November. 20 November

020023 November2300

12.5 7.2 3.5 1.8 0.25 0.25 1.50 0.21 0.80

December 18 December0300

21 December0000

36.1 18.9 2.8 14.4 1.20 1.00 1.70 0.69 0.48

2005January 1 10 January 0100 13 January 2300 11.0 7.2 3.5 0.3 0.75 0.50 0.75 0.48 0.85January 2 22 January 0000 25 January 0200 10.0 6.7 2.7 0.6 0.25 0.25 0.00 0.22 0.10February 1 5 February 0100 8 February 2300 22.8 8.9 3.7 10.2 0.75 0.75 0.25 0.80 0.10February 2 10 February 0200 14 February 0100 24.4 10.7 4.8 8.9 1.00 1.25 1.75 1.22 1.60March 29 March 0100 1 April 0100 33.5 8.3 4.7 20.5 1.75 2.50 2.75 2.23 2.65April 1 17 April 0200 20 April 0200 23.4 6.3 6.7 10.4 1.50 1.50 1.50 1.27 1.50April 2 26 April 0000 30 April 2300 14.5 7.3 7.7 −0.5 0.25 −0.25 −1.00 −0.45 −0.70May 19 May 2300 23 May 0000 36.5 4.8 9.7 22 2.00 2.75 1.50 1.89 1.78June 1 5 June 0100 8 June 0100 14.1 4.2 7.6 2.3 0.75 0.75 0.50 0.64 0.60June 2 24 June 0200 27 June 0200 19.3 5.5 11.3 2.5 0.25 −0.50 0.00 −0.18 −0.35July 24 July 0100 26 June 0200 19.2 0.9 11.9 6.4 0.75 1.75 0.50 1.12 0.93August 13 August 0000 15 August 2300 15.8 0.6 10.5 4.7 0.50 0.25 0.00 0.26 0.15September 10 September

030013 September0000

8.8 0.4 9.6 −1.2 0.50 0.25 0.25 0.62 0.43

October 1 12 October 0200 15 October 0100 19.0 1.6 5.7 11.7 1.25 1.50 0.75 1.25 0.73October 2 29 October 0000 1 November

230015.6 5.0 7.9 2.7 0.75 1.00 0.50 0.66 0.63

November 6 November0100

8 November2300

24.9 15.6 4.6 4.7 1.00 0.50 1.25 0.47 1.00

December 1 December 0100 6 December 0200 65.6 42.0 4.5 19.1 1.25 1.25 2.00 1.53 1.70

Table 1). For each selected event, �θ was calculated for allthree NOAH LSM soil layers by applying a simple subtractionprocess (see Equation (14)) between the soil moisture valuesbefore and after an event. Hence, the correlation between �sand �θ for all 33 selected events was computed for the threesoil layers and the results are shown in Figures 5(a)–(c). Itcan be seen from this figure that there is a good correlationin terms of R2 between �s and �θ at the surface and sec-ond soil layer about (0.67) and (0.70) respectively (Figure 5(a)and (b)). However, the best correlation is achieved at the sec-ond soil layer depth (30 cm) as shown in Figure (5b). This isbecause soil depth has a larger contribution at the second layerin the soil moisture calculations, which in turn offers a bet-ter consistency between changes in the water balance storageand the changes in the calculated soil moisture. Nevertheless,the correlation R2 result over the third soil layer was poor(R2 = 0.3) (see Figure 5(c)). One reason for that is the

vegetation root zone over the Brue catchment is up to 30 cm asit is dominated by grass vegetation type and this in turn controlsthe evapotranspiration rate which is one of the main compo-nents in the water balance calculation. One key function ofplant roots within the soil–plant–atmosphere system is to con-nect the soil environment to the atmosphere by providing a linkin the pathway for water fluxes from the soil through the plantto the atmosphere. Fluxes along the soil–plant–atmosphereinteraction are regulated by above-ground plant properties suchas the leaf stomata, which can regulate plant transpirationwhen interacting with the atmosphere. Therefore, it can besaid that �θ is calculated over a soil profile depth up to40 cm. A combination of the model estimated soil moistureover different layers was conducted in order to account forthe entire depth of the soil column which in turn provideda proper representation of the correlation between the changein the water balance storage and the change in the NOAH



Table 2. Results summary of �s (change in the storage) and �θ (changes in the MM5-NOAH LSM soil moisture estimation) over three soillayer depths for the Brue catchment in (2006).

Flowevent

Duration Totalrain

Run offvolume

TotalET

�S

(mm)�θ (%) at single

LSM layers�θ (%) at combined

LSM layers(mm) (mm) (mm)

2006 Start date, time End date, time (0–10)cm

(10–40)cm

(40–100)cm

(0–40)cm

(0–100)cm

January 30 December2005 0000

5 January 20060200

33.6 16.8 3.8 13.0 0.38 0.41 0.56 0.39 0.49

February 14 February 0100 19 February 0200 26.2 11.8 3.4 11.0 1.00 0.31 1.34 0.48 0.99March 1 7 March 0000 13 March 0200 30.8 14.4 4.6 11.8 0.85 0.76 −1.47 0.78 −0.57March 2 26 March 0100 28 March 2300 8.4 4.1 4.0 0.3 −0.50 −0.15 0.71 −0.24 0.33April 29 March 2300 4 April 0200 21.4 7.7 6.9 6.8 −0.25 0.04 −0.03 −0.03 −0.03May 21 May 0000 31 May 0200 65.8 21.4 10.2 34.2 1.25 1.58 2.11 1.49 1.86June 25 June 0200 29 June 0100 19.2 1.7 12.1 5.4 −0.50 −0.41 −0.29 −0.43 −0.35July 5 July 0100 10 July 0000 39.0 1.8 14.6 22.6 0.35 0.88 −1.42 0.75 −0.55August 28 August 0100 31 August 2300 20.6 1.3 13.3 6.0 −1.00 −0.33 −0.08 −0.49 −0.25September 28 September

23005 October 0200 27.6 2.5 9.9 15.2 1.00 1.80 1.60 1.60 1.60

October 19 October 0000 28 October 0200 77.8 28.1 6.4 43.3 3.25 4.34 6.57 4.07 5.57November 23 October 0000 29 October 2300 51.0 33.7 4.2 13.1 1.25 0.99 3.87 1.05 2.74December 1 3 December 0100 6 December 0000 15.2 9.3 3.3 2.6 0.75 0.47 0.89 0.54 0.75December 2 10 December

020015 December0200

18.3 14.3 2.9 1.1 0.00 0.51 0.00 0.38 0.15

Figure 5. Correlation between the change in the storage and the change in the MM5/LSM soil moisture in 2004 and 2005 at (a) surface layer(0–10 cm); (b) second layer (10–40 cm); (c) third layer (40–100 cm); (d) combined layer (first and second) and (e) combined layer (first,

second and third) layers of NOAH LSM.

LSM soil moisture estimation. Therefore, two additional caseswere considered by combining soil moisture estimations fromdifferent layers. In the first case the estimated soil moisturefrom the first two layers (first and second) of the NOAHLSM was computed following Equation (7), so that the com-bined soil moisture represents the volumetric water contentin the soil column depth up to 40 cm. In the second case,the estimated soil moisture from the first three layers (first,

second and third) of the NOAH LSM was computed follow-ing Equation (8), so that the combined value of soil moisturerepresents the volumetric water content in the soil depth upto 100 cm. The same aforementioned process used to assessthe estimated soil moisture from NOAH LSM as single lay-ers was applied to assess the combined soil moisture producedfrom these two new cases and their results are presented inFigures 5(d) and (e).



Figure 6. Volumetric soil moisture (VSM) time series from the MM5-NOAH LSM in 2006 for: (a) three single layers with soil depths:(surface layer (0–10 cm), second layer (10–40 cm) and third layer (40–100 cm)); (b) two cases of layers combinations (first and second)layers and (first, second and third) layers; (c) AMSR-E satellite soil moisture time series. This figure is available in colour online at

wileyonlinelibrary.com/journal/met

It is obvious from Figure 5(d) that R2 is higher around 0.75if we combine soil moisture layers 1 and 2 rather than usingindividual soil moisture layers (see Figure 5(a) and (b)). In thiscase, both the catchment water storage �s calculated from thewater balance equation and the combined soil moisture �θ

from the first and second soil layers of the NOAH LSM betterrepresent the change in the water content over a soil columndepth up to 40 cm This result clearly indicates the vital roleof taking into account several soil layers for assessing andevaluating soil moisture estimations. On the other hand, if wecombine the three top soil layers with a total soil depth of100 cm with the catchment water storage, the results are poorer(R2 = 0.41). This is mainly because of the vegetation root zonelimitation as discussed above.

A similar discussion can also explain the results producedfor the validation year 2006 (see Figures 6 and 7, and Table 2).Figure 7 clearly shows that the combination of the first twolayers of the NOAH LSM in 2006 produced the best correlation(R2 = 0.74) between �s and �θ (see Figure 7(d)). As thesimulation has been run for 1 year, it is important to examinethe significance of the linear correlation between the event-based calculations between the changing in the water storageand the changing in the estimated volumetric soil moisturefrom the MM5-NOAH LSM. Therefore, a statistical t-test wasconducted to examine the significance of the resulted linearcorrelation between �s and �θ . A usual significance level of0.05 was adopted in this study and the p-values are computedfor every case. All the estimated p-values were shown to bebelow the significance level, which in turn indicates that there

is a significant linear correlation between �s and �θ althoughthe results from layer 3 as a single soil layer do not showa good result (R2 = 0.37) due to the vegetation root zonelimitation (see Figure 7(a)–(e)). It is important to mention thatthe correlation was used as a performance indicator given thefact that the units of �s and �θ are in mm and m3 m−3

respectively and the use of other performance measures suchas the root mean square error would not be appropriate.

Soil moisture content usually increases when there is morefree water produced from high rainfall rates. As a result, �sincreases in the winter season which in turn cause increasesin �θ while in the summer season �s decreases causinga decrease in �θ . However, the evapotranspiration plays animportant role in controlling the mean flow and in turn thechange in water storage �s. Overall, �θ increases with �sover the three MM5-NOAH LSM layers whether in singleor combined mode. Table 1 shows that at the surface soillayer (0–10 cm), the highest values for �s and �θ werefound during the winter and spring due to the high rainfallrate and low evapotranspiration rate which in turn lead to anincrease in soil water storage (see events December 2005 andMarch 2004 and 2005). A similar condition was also observedover the second soil layer (10–40 cm). However, a significantincrease in the �s values was observed during the summerfor some particular events such as in June and July 2004 dueto the rainfall rates exceeding the evapotranspiration rate andconsequently increasing the value of �s over the catchment.In regards to the third soil layer (40–100 cm), Table 1 showsthat the �θ values did not change over some particular events



Figure 7. Correlation between change in the storage and change in the MM5/LSM soil moisture in 2006 at (a) surface layer (0–10 cm); (b) secondlayer (10–40 cm); (c) third layer (40–100 cm); (d) combined (first and second) and (e) combined (first, second and third) layers of NOAH LSM.

despite of the significant change in the corresponding �svalues (see events February, March and October 2004). Thisindicates that �θ is not representative of the soil moisturewithin the third soil layer. One of the potential reasons isthat the evapotranspiration rate is controlled by the catchmentvegetation root zone which is extended in the soil up to 30 cmdue to the type of the dominant vegetation over the Bruecatchment.

In terms of soil layer depth, Table 1 shows that the deeperthe soil layer is the higher the water content is available inthe winter season while the inverse occurs during the summerseason as the water content level becomes lower when the soillayer becomes deeper. Consequently, �θ has the highest valuesin winter and the lowest values in the summer particularly atthe third layer of LSM (40–100 cm). When a combination ofdifferent soil moisture layers is used, similar results are shown(see Table 1), with higher values of �θ in winter and lowervalues of �θ in summer. The results of combining layers 1–2with a depth of 40 cm were better than those combining layers1–2–3 with a depth of 100 cm due to the vegetation rootzone limitation that affects the evapotranspiration rates andin turn the estimated soil moisture over the soil layer depth.Similarly, the results from 2006 using single and combinedNOAH LSM soil layers (see Table 2) corroborate the resultsexplained above.

In this study, the estimated soil moisture from the coupledNOAH LSM with the numerical weather model MM5 overthree single soil layers depths agrees with the findings of Konget al. (2011) in terms of the consistency of the predicted soilmoisture for all the three LSM soil layers with the observedrainfall and evapotranspiration as shown in Figure 4, althoughthe study by Kong et al. (2011) was conducted using anuncoupled land surface model known as MOSES.

4.2. Intercomparisons against AMSR-E soil moistureretrieval

Three years of descending dual polarized AMSR-E brightnesstemperatures at 6.9 GHz were converted into the volumetricsoil moisture vsm using the Land Parameter Retrieval ModelLPRM. As a result, the simulated MPDI time series wasiteratively computed through the LPRM for the range ofvsm (0–0.80) and the retrieved soil moisture over the Bruecatchment can be seen in Figure 4(e) in the context of timeseries. This figure shows that on average, the mean annualvolumetric soil moisture is around 10.5%. The lowest valueswere found during the summer (May, June and July), whichwere around 5%. Larger values (around 47%) were foundduring the winter (December, January and February). Thetwo surface roughness parameters h and Q were calibratedusing the water storage change. Two years (2004–2005) ofhydrological data were used for calibration of these parametersand 1 year (2006) data was used for validation purposes. Theoptimal h and Q parameters were found by maximizing thecorrelation between �s and �θ . The correlation results duringthe calibration and validation phase are shown in Figure 8(a)and (b) respectively. It can be seen from those figures thatthere is a good correlation (R2 = 0.74) and (R2 = 0.71)between changes in the storage and changes in the AMSR-E soilmoisture for calibration and validation datasets respectively.

In terms of comparing the MM5-NOAH LSM and theAMSR-E soil moisture estimations for hydrological applica-tions, the results from the 2006 simulations are adopted (seeFigures 6 and 7(d)). The combination of the first two layersof the NOAH LSM (see Figure 6(b)) was used due to its bestperformance in terms of correlation between �s and �θ . There-fore, those results were compared with the validation results ofthe satellite surface soil moisture estimation (see Figure 8(b)).



Figure 8. Correlation relationship between change in the storage �s andchanges in AMSR-E soil moisture �θ for all events included within:

(a) calibration and (b) validation data sets.

The comparison result shows that the correlation result between�s and �θ in the MM5-NOAH LSM was slightly better thanthat predicted from the AMSR-E satellite. However, both meth-ods agreed that the proposed scheme of water balance appli-cation can potentially be a useful tool in the assessment andvalidation process of the soil moisture estimation from bothsatellite and land surface model that coupled to a numericalweather model.

Four important issues have to be taken into account whencomparing the MM5-NOAH LSM soil moisture estimationagainst the AMSR-E satellite soil moisture retrieval, which areas follows.

1. Soil depth. One significant advantage of conducting MM5-NOAH LSM for estimating long term soil moisture timeseries in comparison with satellite soil moisture estimationis the use of deep soil depths in the former technique (i.e.applicability of estimation soil moisture over three layerswith different depths 10, 30 and 60 cm) and the abilityto combine these layers. The AMSR-E soil measurementreflects only the moisture content of the microwave soilmoisture sampling depth which is at most within a rangeof 0–2 cm due to the microwave wavelength limitations.Therefore, only the surface soil layer can be detected by thesatellite techniques which in turn pose a significant limitationon the use of these soil moisture observations despite theimportance of the surface soil moisture in some particularapplication such as hydrology and agriculture.

2. Horizontal resolution. Soil moisture estimations representaverage values over the horizontal retrieval area (i.e. area-averaged values) in both approaches for estimating soilmoisture. The coupled MM5-NOAH LSM used several highresolution fields that characterize land surface conditions(Chen and Dudhia, 2001) such as vegetation, water andsoil characteristics at fine scales and capture the dynamics

of the associated land surface forcing in order to meet theincreasing demand for employing high resolution mesoscalemodels up to 1 km horizontal resolution and in turn offershigh resolution soil moisture estimations. In comparison,the AMSR-E satellite data provided surface soil moistureresult at 25 km horizontal resolution which is good forglobal retrievals. However, soil moisture results retrievedfrom the AMSR-E over the Brue catchment showed a goodconsistency with the observed rainfall and flow.

3. Range of soil moisture values. Figures 4 and 6 have shownthat the range of soil moisture values was large andsignificant in the retrieved soil moisture from the AMSR-E satellite between (5%) as a minimum value and (48%) asa maximum value in comparison with those estimated fromthe MM5-NOAH LSM. One potential reason is the timedrift problem (i.e., error accumulation) that is not present inthe satellite measurements as the satellite provides an instantmeasurement of brightness temperatures of the soil surface.Hence, the satellite measurement represents the instant stateof the surface soil moisture at a particular time. It is foundthat despite of the existence of some high rainfall rateduring the summer season, surface soil moisture observedlow values as would be expected in such warm weather. Incontrast, the calibration and validation results of the MM5-NOAH LSM combined layer estimation in Figures 4(d) and6(b) showed that the soil moisture range is small between(23.8%) as a minimum value during summer and (42.5%)as maximum value during winter. As a result, it is obviousthat the MM5-NOAH LSM soil moisture values are largerthan the corresponding values retrieved from the AMSR-E satellite which in turn stresses the existence of the timedrift problem in the hydrological and land surface modelsapplication.

4. Assessment and validation scheme. It was mentioned atan early stage of this study that there is a lack of val-idation datasets that are spatially representative of soilmoisture observations. This was the motivation to proposehydrological-based schemes for assessing the soil moistureestimation from the AMSR-E satellite and the MM5-NOAHLSM. Event-based water balance serves well in assessingthe estimated soil moisture from the AMSR-E satellite asit was shown by the good correlation between �s and �θ

(see Figures 8(a) and (b)). In contrast, the combined prod-uct (40 cm depth) of the MM5-NOAH LSM soil moistureshowed a slightly better correlation between �s and �θ (seeFigures 5(d) and 7(d)). Therefore, the combined product ofsoil moisture estimations from the MM5-NOAH LSM out-performs the soil moisture estimations from the AMSR-Esatellite.

5. Conclusions

Soil moisture estimation is a key variable in many disciplinesespecially in the hydrological and meteorological fields. In thisstudy, a coupled NOAH LSM with the mesoscale model MM5was adopted to estimate soil moisture at three soil layer depths(10, 30 and 60 cm) as a first attempt to estimate soil moisturefrom a coupled land surface model for hydrological purposes.As a case study, the coupled NOAH LSM was applied to avegetated site (the Brue catchment) located in the South Westof England. The results revealed that in general the three layersoil moisture estimation of the MM5-NOAH LSM had a goodagreement with the observed rainfall and evapotranspiration



dynamics. However, comparing the MM5-NOAH LSM soilmoisture simulations with the available observed rainfall andevapotranspiration is not adequate to assess the estimated soilmoisture performance. Soil moisture estimation assessment andvalidation is a challenge in the hydrological community asit is difficult to measure accurately in both time and space.From the fact that hydrologists are primarily interested inrunoff and water budgets, a new empirical scheme based onthe water balance change that has been firstly proposed byAl-Shrafany et al. (2012) was applied in this study to assesssoil moisture retrieved from the MM5-NOAH LSM. Two setsof numerical experiments were conducted for several casesfor 2004 and 2005. Three layers of soil moisture values atdifferent depths (10, 30, and 60 cm) were retrieved fromthe configured simulations. The results from the inner MM5domain have a 12 km spatial resolution, which is consideredrepresentative of the study catchment area (135 km2). Thechange in the soil water storage calculated for selected rainfall-flow events was used to assess the estimated soil moisturefrom the coupled model. Basically, the calculated �s over thecatchment considers the total depth of soil by describing theflow of water in and out of a column of soil. Therefore, itis concluded from this study that in order to have a properrepresentation between the calculated water storage and theestimated soil moisture from the MM5-NOAH LSM layers, itis important to account for the key soil depth of the soil columnwhen calculating changes in the estimated soil moisture. Hence,it was found that combining the first two soil moisture layersof the LSM produces a better result in terms of the correlationbetween �s and �θ than either the first or second layercould produce. It is important to mention that the vegetationroot zone, which is a function of the vegetation type, hasan effective impact on the correlation result between �s and�θ over different depths of soil layers through its significantcontribution into the ET calculation and consequently into thesoil moisture calculations over the soil layer depth. Hence, ifthe vegetation root zone has a limited depth into the soil layer, aco-efficient of poor performance would be generated, while theinverse is also correct. The proposed scheme application offersa promising opportunity for further assessing soil moistureestimations from coupled LSMs and taking advantage of theabundance of hydrological data.

As a comparison study and according to the achievedcorrelation between �s and �θ used as a model performanceindicator, it has been shown that the MM5-NOAH LSMoutperforms the AMSR-E satellite when the proposed scheme isapplied for soil moisture assessment. In fact, the MM5-NOAHLSM combined soil moisture with a depth of 40 cm was betterthan remote sensing techniques in retrieving ‘soil moisture’although it suffers from a time drift problem which is a commonproblem occurred in hydrological models. However, accountingfor soil layer depth is the key advantage in the performance ofthe MM5-NOAH LSM soil moisture estimations.

Overall, it is concluded from this study that the empiricallyproposed scheme offers a useful validation tool and couldplay a valuable role in the assessment and evaluation of soilmoisture estimated from the MM5-NOAH LSM and AMSR-E satellite data for hydrological purposes as the presentedresults were positive and promising. However, there are stillmany knowledge gaps that need further research. For example,the strong synergy between satellite soil moisture sensing andnumerical weather modelling may enable us to make full useof the available information and produce soil moisture with afurther improved accuracy (i.e., a data fusion approach).

Acknowledgement

The authors would like to thank the Ministry of Higher Edu-cation and Scientific Research MOHER in Iraq for fundingthis research. We also thank the UK Met Office, the Environ-ment Agency and the British Atmospheric Data Centre (BADC)for providing the hydrological and meteorological data. Alsothanks go to the European Centre for Medium-Range WeatherForecasts ECMWF for providing the Era-interim reanalysisdata. The two anonymous reviewers and editor have pro-vided constructive comments which significantly improved thismanuscript.

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