HYDROLOGICAL PROCESSESHydrol. Process. (2013)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hyp.9925

Complexity-reduction modelling for assessing the macro-scalepatterns of historical soil moisture in the Euro-Mediterranean

region

Nazzareno Diodato,1 Luca Brocca,2 Gianni Bellocchi,1,3* Francesco Fiorillo4

and Francesco Maria Guadagno41 Met European Research Observatory, Benevento, Italy

2 Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy3 Grassland Ecosystem Research Unit, French National Institute of Agricultural Research, Clermont-Ferrand, France

4 Environmental Geology Department, University of Sannio, Benevento, Italy

*CFreE-m

Co

Abstract:

Complexity-reduction modelling can be useful for increasing the understanding of how the climate affects basin soil moistureresponse upon historical times not covered by detailed hydrological data. For this purpose, here is presented and assessed anempirical regression-based model, the European Soil Moisture Empirical Downscaling (ESMED), in which different climaticvariables, easily available on the web, are addressed for simplifying the inherent complexity in the long-time studies. Toaccommodate this simplification, the Palmer Drought Severity Index, the precipitation, the elevation and the geographicallocation were used as input data in the ESMED model for predicting annual soil moisture budget. The test area was a large regionincluding central Europe and Mediterranean countries, and the spatial resolution was initially set at 50 km. ESMED modelcalibration was made according to the soil moisture values retrieved from the Terrestrial Water Budget Data archive by selectingrandomly 285 grid points (out of 2606). Once parameterized, ESMED model was performed at validation stage both spatiallyand temporally. The spatial validation was made for the grid points not selected in the calibration stage while the comparisonwith the soil moisture outputs of the Global Land Data Assimilation System–NOAH10 simulations upon the period 1950–2010was carried out for the temporal validation. Moreover, ESMED results were found to be in good agreement with a root-zone soilmoisture product obtained from active and passive microwave sensors from various satellite missions. ESMED model was thusfound to be reliable for both the temporal and spatial validations and, hence, it might represent a useful tool to characterize thelong-term dynamics of soil moisture–weather interaction. Copyright © 2013 John Wiley & Sons, Ltd.

KEY WORDS soil moisture; European Soil Moisture Empirical Downscaling (ESMED) model; long-term analysis; Euro-Mediterranean region

Received 5 December 2012; Accepted 28 May 2013

INTRODUCTION

Soil moisture has an important influence on land–atmosphere feedbacks at climatic time scales, because ithas a major effect on the partitioning of incomingradiation into latent and sensible heat fluxes and on theallocation of precipitation into runoff, subsurface flowand infiltration (WMO-IOC, 2010). Changes in soilmoisture have a serious impact on flood and landslidehazard, meteorological forecasting, agricultural productiv-ity, forestry and ecosystem health. In particular, drynesspatterns are very critical in drought periods, whenavailability of water in the soil decreases as evapotrans-

orrespondence to: Gianni Bellocchi, Grassland Ecosystem Research Unit,nch National Institute of Agricultural Research, Clermont-Ferrand, France.ail: giannibellocchi@yahoo.com

pyright © 2013 John Wiley & Sons, Ltd.

piration depletes the water supply. On the other hand, wetsoils become a critical condition when the soil moisture isinvolved within the runoff and erosivity processes(Diodato and Bellocchi, 2012). Therefore, there is apressing need for measurements/estimates of soil moistureover large areas to be employed for operational andscientific applications.Soil moisture can be estimated from in situ measure-

ments, remote sensing and physical models. In situmeasurements provide the most accurate methods but arelimited in terms of spatial extent (e.g. Brocca et al.,2010a). Records of soil moisture content measured in situare available for only a few regions and are often veryshort (Robock et al., 2000), thus being not sufficientlyreliable to determine spatial and temporal trends (IPCC,2007). Recently, an effort to expand and harmonize soilmoisture measurement network has been made to

N. DIODATO ET AL.

establish and maintain a global in situ soil moisturedatabase (the International Soil Moisture Network athttp://www.ipf.tuwien.ac.at/insitu, Dorigo et al., 2011).The difficulty of measuring soil moisture on the groundhas motivated considerable research in the field of remotesensing to retrieve soil moisture (e.g. Moran et al., 2004;Wagner et al., 2007). Nowadays, several microwaveactive and passive sensors are able to provide satellite soilmoisture products at coarse spatial scale (~25 km), dailytemporal resolution and with good accuracy (Broccaet al., 2011; Albergel et al., 2012; Parrens et al., 2012).The sensors currently operational are: the AdvancedSCATterometer on board Metop satellite (Bartalis et al.,2007); the Windsat polarimetric radiometer on boardCoriolis satellite (Parinussa et al., 2012), the MicrowaveImaging Radiometer with Aperture Synthesis on boardSMOS satellite (Kerr et al., 2010) and the TropicalRainfall Measuring Mission (TRMM) Microwave Imageron board TRMM satellite (Owe et al., 2008). However,the low penetration depth (less than 5–10 cm) of thesesensors might limit the use of satellite products for theroot-zone soil moisture estimation, even though a recentinitiative on this direction has been started (http://www.esa-soilmoisture-cci.org). On the other hand, physicalland surface models simulating the space–time variabilityof soil moisture could be used (e.g. Famiglietti and Wood,1994), but they usually require a significant parameter-ization that may introduce a high degree of uncertainty ofthe model predictions (Beven, 2008), mainly if detailedinputs are not available.Therefore, simplified statistical models might be

desirable because they use a small number of inputs torepresent the most important spatial and temporal controlson soil moisture. Simplified models have not the aim tocharacterize in detail the causal mechanisms by which aphysical variable may manifest itself. They focus ratheron the dynamics of physical systems. Moreover, theapplication of these models is very easy, and the requiredcomputation resources (runtime and memory) are verylimited. Therefore, this complexity-reduction modellingapproach can be useful for increasing the understandingof how the climate affects the basin soil moistureresponse, upon multiple spatio-temporal scales. In thisway, the time and difficulty in using a model are weightedvery few against the benefits of using the model itself.Thus, the consideration is not only in the context of howwell the model fits data but how well the model serves itsintended purpose (Toy et al., 2002) as, for instance, aguide in the historical extrapolation. For instance,Diodato and Bellocchi (2008a, b) and Diodato et al.(2010) approached both the regional, sub-regional andlocal scales for drought indicators and vegetationgreenness over central Mediterranean area. In thesestudies, it was underlined that the climatic forcing are

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involved in a large spectrum of hydrological patterns that,in turn, result affected from soil moisture considerably,both over time and geographically. Additionally, severalauthors found that local soil moisture observationscontain information about the large-scale spatial-temporalvariability (Martinez-Fernandez and Ceballos, 2003;Blöschl, 2005; Brocca et al., 2012). Therefore, thanksto the spatial scaling properties of soil moisture,simplified regression model can be used also fordownscaling/upscaling soil moisture simulation at differ-ent spatial scales thus exploiting also the spatial statisticsof soil moisture (Blöschl et al., 2009).On this basis, the study presented here is intended to

develop a parsimonious soil moisture model based onsimple and widely available input data: precipitation,Palmer Drought Severity Index (PDSI), elevation andgeographical coordinates. The model, named EuropeanSoil Moisture Empirical Downscaling (ESMED), isintended to investigate how climate forcings influencethe spatial and temporal variability of soil moisture at themacro-scale for the Euro-Mediterranean region. Thegeographical representation chosen at a scale correspond-ing to Europe and Mediterranean Africa (intermediate onhemispheric and local scales) guarantees distinct proper-ties of the sub-continental scale while preserving theparsimoniousness of the soil moisture model. In this firstphase, the ESMED model was evaluated at annual timescale upon a climatic period (1950–1999) including bothwet and dry years.

STUDY AREA AND DATA SOURCES

The Euro-Mediterranean study area (referred to as EMAhereafter) includes the central Europe and Mediterraneanarea (Figure 1). The variegated morphology of the EMAregion has important consequences on both sea andatmospheric circulations, and determine a non-uniformdistribution of weather types (Lionello et al., 2006), whichis reflected in a variegated pattern of wet-and-dry lands.Across EMA, the soil moisture dataset retrieved by the

Terrestrial Water Budget Data archive (TWBD), andprovided by the Atlas of the Biosphere (http://www.sage.wisc.edu/atlas) was used (Willmott and Matsuura, 2001). Inparticular, these data were derived by applying a monthlywater-budget methodology (Willmott et al., 1985b)with grid points covering the globe, and incorporating aDEM-climatologically assisted interpolation methods(Willmott et al., 1985a). The dataset comprises monthlysoil moisture budget values (mm of water) averaged overthe period 1950–1999 with 0.5� spatial resolution andassuming a constant soil water-holding capacity valueequal to 150mm. For this study, the annual soil moisturebudget value was computed and used for the model spatialcalibration (see Figure 1).

Hydrol. Process. (2013)DOI: 10.1002/hyp

Figure 1. Annual soil moisture map (mm) averaged over the period 1950–2000 obtained by the Terrestrial Water Budget Data archive for theEuro-Mediterranean area. The grid cells used for the ESMED model spatial calibration (white circles) and for the temporal validation (black

squares) are also shown

MACRO-SCALE SOIL MOISTURE ESTIMATION IN THE EURO-MEDITERRANEAN REGION

For the model temporal evaluation, the Global LandData Assimilation System (GLDAS)–NOAH10 soilmoisture simulations provided by the Hydrology portalsof the Giovanni Web-based NASA application (http://disc.sci.gsfc.nasa.gov/giovanni) were used. Specifically,the simulated annual soil moisture values for the layerdepth between 0 and 40 cm were taken by weightingaveraging, according to the layer depth, the simulateddata for the first two layers (0–10 and 10–40 cm) for somespecific locations across Europe and covering the period1950–2010.Additionally, a satellite-derived dataset was used for

an independent temporal evaluation based on observa-tions. The dataset developed within the WAter CycleMulti-mission Observation Strategy project (http://www.esa-soilmoisture-cci.org), was released in June 2012within the framework of the Climate Change Initiative(CCI, http://www.esa-cci.org). It is the first availablelong-term remotely sensed surface soil moisture productcovering a 32-year period from 1978 to 2010, providingdata in 0.25� spatial resolution and was generated bymerging active and passive soil moisture records fromvarious satellite missions (Liu et al., 2011, 2012).Merging different instruments from various satellitesbased on their temporal availability causes an increase ofdata quality with time. The temporal resolution isapproximately 1–3 days. As satellite observations are onlyrepresentative of a thin soil layer (<5 cm), a root-zone soilmoisture product (named Soil Water Index, SWI) wasderived by applying a semi-empirical approach as proposed

Copyright © 2013 John Wiley & Sons, Ltd.

by Wagner et al. (1999) and setting the value of the Tparameter (characteristic time length) equal to 5 days(Brocca et al., 2011). The reader is referred to Wagneret al. (1999) or Brocca et al. (2010b) for a more detaileddescription of the exponential filter.As input data for the ESMED model, the PDSI and

precipitation data were derived from Climate ResearchUnit self-calibrating PDSI (Wells et al., 2004), andGlobal Precipitation Climatology Centre (GPCC V5analysis-land), respectively. Both datasets are providedvia Climate Explorer (http://climexp.knmi.nl) at 0.5�

� 0.5� resolution. Topographic variables were, instead,derived from the Global Multi-resolution TerrainElevation Data 2010 (GMTED2010) and supplied byEarth Resources Observation and Science (EROS)Centre (http://eros.usgs.gov). In order to overcomeproblems associated with bias-and-smoother values thatcan occur at higher spatial resolution (about 1 km),elevation data were resampled with grid cells of 10 km(consistent with the resolution of the hydrologicalvariables used in this study).

MULTIVARIATE MODEL OF SOIL MOISTURE: THEESMED MODEL

Overall model structure

The main idea for the development of the ESMEDmodel stems from the principle of GeographicallyWeightedRegression (e.g. Fotheringham et al., 1998), a multivariate

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version of spatial regression models, generating a shapeaccording to the following equation:

YT uð Þ ¼ XTc uð Þ � B (1)

where the predictand matrix and the predictor matrix(YT and XT) have the same number of temporal samples (t),but may have different spatial representations (different gridor number of locations); c is the vector of parameters in theregression, z is a noise terms and the vector u is the locationcoordinates.In this study, the same principle was expanded to

achieve a satisfactory solution in which soil moisture andthe geographical control are modelled together to accountfor temporal and spatial dependence of predictand. In thisway, it is assumed that soil moisture values can bemodelled as a function of elevation and geographical datatogether with two quantities varying in time: the PDSIand the precipitation. Although the PDSI could be not anoptimal index, since it does not include variables such aswind speed, solar radiation, cloudiness and water vapour,it is widely used and freely available via web resources.Moreover, these data are available for most parts of theglobe since 1870, as well as for the precipitation, thusgiving the possibility to obtain historical soil moisturetime series upon a period longer than a century. Based onthis understanding, the ESMED model was developed.In particular, the conceptual–statistical model was

resolved into additive terms by applying a multivariatenon-linear regression with the following structure:

ESMED ¼ Κ�LSC þ Ω�DSC þΜ (2)

where ESMED is the simulated soil moisture value (mm);LSC is the large-scale component governing the soilmoisture patterns, while the DSC is the downscalingcomponent; K (mm���1) and ٠(mm�0.5) are twocoefficients, and M (mm) is a shift parameter. ESMEDmodel can be applied at daily, monthly or yearly timescale according to the time step of the input variables. Inthis work, the annual scale was considered, and eachvariable corresponds to georeferenced data on x, ycoordinates.

Modelling assumptions

Given that the assumptions that underlie a model mustbe well understood and explicitly stated with reference tothe conditions under which they are valid (Mulligan andWainwright, 2004), the rationale followed for buildingthe ESMED model is given below.The first component of Equation (2), depending on the

geographic location too, is given by:

LSC ¼ Lat � Longð Þ�PDSI (3)

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where Lat and Long are the Latitude and Longitudecoordinates (�), respectively, and PDSI is the PalmerDrought Severity Index (Alley, 1984), dimensionless.The product in Equation (3) illustrates how relation-

ships between the soil moisture and the hydro-climateexplanatory variable, i.e. the PDSI, varies geographically.In particular, latitude and longitude in Equation (3) havepositive and negative signs, respectively. Such a linearrelationship is indeed a simplification to represent that thepositive correlation with latitude is mainly due to thedecrease of temperature and, hence, of the potentialevaporation flux at northern latitude that produces highersoil moisture values. On the other hand, as continentaland eastern Europe lands are generally less humid(Seneviratne et al., 2006), as compared with the oceanicand Mediterranean Europe, affected by westerly rainyflows, a negative correlation with longitude is expected.At this stage, the domain of validity of Equation (3) is inthe range of latitudes (from 35� to 55� North) andlongitudes (from 10� West to 35� East) represented inFigure 1.The second component of Equation (2) is explicited as:

DSC ¼ffiffiffiP

p�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiDEM þ n

n

r(4)

where P is annual precipitation (mm), DEM is theelevation (m) and n (m) is an elevation shift parameter.The shift parameter n, in the downscaling component,

Equation (4), is to modulate together the precipitation andelevation variables. Although these two attributes aregenerally well correlated with each other, their mutualinfluences vary in order of the considered spatial scale.Therefore, precipitation and elevation considered togetherrealize a better correlation with the TWBD soil moisturedata than taken individually (Diodato, 2005). Addition-ally, as the soil can reach the saturation before rainfallattained the maximum amount in a given time interval,the rainfall term in Equation (4) was put under the squareroot (Penna et al., 2011).The spatial support of rainfall and elevation can be

much smaller than the one of the PDSI and the spatialcoordinates. Therefore, rainfall and elevation are includedin the downscaling component to map the soil moisturevalues at finer spatial scales (e.g. 1–10 km) than the oneused in this study for the model development (50 km).However, only spatial scales of soil moisture dependingfrom atmospheric forcing effects (Cayan andGeorgakakos, 1995) can be assessed by the ESMEDmodel. Several authors showed, in fact, that the soilmoisture spatial variability at smaller scales (<1 km) isdriven by vegetation, soil type and local topography(Vinnikov et al., 1999; Entin et al., 2000; Brocca et al.,2012), which are not accounted by the ESMED model.

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MACRO-SCALE SOIL MOISTURE ESTIMATION IN THE EURO-MEDITERRANEAN REGION

The above assumptions cannot explain underlyingmechanisms in all its particulars, which may even changeover future time scales. However, because of their highlevel of generality, they are likely to hold true under, forinstance, envisioned climate change scenarios based onglobal climate modelling. Finally, we note that, whiledeveloping the model, we tested the relevance of eachvariable in Equations (2)–(4) obtaining that the currentstructure of the ESMED model provides significantlybetter results.The scale parameters, K and Ω in Equation (1), are

coefficients that can be conveniently assumed constantover time and space, since they are used to convertmeasure units of the two terms (large- and small-scalespatial components). Also, the shift parameter M is takenas a constant, but it assumes physical values because itrepresents the reference-water content in the soil,comparable to a long-term minimum climatologicalvalue. The model parameters were optimized by an initialco-iterative calibration process between the compositeexplanatory variables (LSC and DSC) against TWBD soilmoisture budget values. The selected goodness-of-fitcriterion was the Nash–Sutcliffe efficiency index, NS,(Nash and Sutcliffe, 1970) but also the correlationcoefficient, R, the root mean square error, RMSE, andthe mean absolute error, MAE, were used for theassessment of the model performance. Independence oferrors due to the possible presence of significant serial

Table I. Parameter values of the ESMED model estimated fromthe calibration dataset (for symbols see text)

Parameter Value CI 95% p-value

K 0.313 [0.232–0.393] <10�9

Ω 2.399 [2.148–2.650] <10�9

M 10.239 [2.036–18.441]

Figure 2. Scatterplot between soil moisture data obtained by the Terrestrial Wa) calibration, and b) validation pixels (R: correlation coefficient, NS: Nash–

Copyright © 2013 John Wiley & Sons, Ltd.

autocorrelations among the residuals was also testedthrough the Durbin–Watson statistic test.

RESULTS AND DISCUSSION

In this section, first, the comparison between the TWBDand the ESMED soil moisture data is presented anddiscussed. Then, the soil moisture data derived by theGLDAS–NOAH10 system and the satellite-derived root-zone soil moisture product, SWI, are employed for a robustvalidation of the ESMED model on both the temporal andspatial scales.

Spatial analysis

Across EMA, 285 grid points were randomly selectedfor the spatial calibration of the ESMED model (whitecircles in Figure 1). By using this calibration dataset, weinitially tested if the ESMED model can be simplified bytaking individually the two terms (LSC and DSC) ofEquation (2). The obtained p-values were found to be lessthan 10�9 for both components thus assuring theirstatistical significance at a confidence level higher than99%. As a matter of fact, if a term is omitted Equation (2)produces strongly biased estimates (data not shown).Consequently, both terms are used in the ESMED model.Similar analysis (not shown) was carried out by removingeach single variable (PDSI, spatial coordinates, rainfalland elevation) from the model but, as before, eachquantity was found to be statistically significant. Theoptimized parameters of Equation (2) determined againstTWBD soil moisture data are given in Table I whileFigure 2a shows the calibration results for 285 datapoints. As it can be seen, negligible departures of the datapoints from the 1:1 line are observed. The modelcalibration was also evaluated based on the fit criteriamentioned above. The NS efficiency index and thecorrelation coefficient, R, are equal to 0.61 and 0.76,

ater Budget Data (TWBD) set (obs) and the ESMED model (sim) for the:Sutcliffe efficiency index, RMSE: root mean square error, N: sample size)

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respectively, and can be considered satisfactory. Thehistogram of residuals (not shown) manifests a quasi-Gaussian pattern, and also the corresponding quantile–quantile plot appears to accommodate residuals accordingto a normal distribution. These results indicate that thepredicted data are free from significant biases andoutliers. The MAE is very low (12.87mm), roughlyabout 15% of the soil moisture mean, averaged upon the285 data points, and below the corresponding standarddeviation, that is of 25.0 mm. The Durbin–Watsonstatistic test of the residuals was also produced to evaluateif there is any significant correlation based on the order inwhich they occur. Since the p-value is greater than 0.05,there is no indication of serial autocorrelation in theresiduals at the 95% confidence level.Successively, the model was validated with the

remaining 2321 data points not considered for itscalibration (see Figure 2b). The results confirm therobustness of the ESMED model as the performancestatistics remain quite unchanged (NS = 0.57 andR= 0.73). As it can be seen in Figure 2b, the TWBDdata show a maximum threshold values at 150mm thatcorresponds to the soil water-holding capacity value usedfor the construction of this dataset. On the other hand, theESMED model shows values also higher than 150mmthat, however, could be assumed reasonable consideringthe large heterogeneities of the EMA region.

Temporal analysis

For the temporal validation, six locations (see Figure 1) areselected across EMA in order to cover all the study area andpreserving the climate zone characteristics and variability atmultiple spatial scales. Soil moisture validation data were

Figure 3. Comparison between the normalized (between 0 and 1) time series of aand theGLDAS-NOAH system for the six sites shown as black square in Figure 1

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extracted from theGLDAS–NOAH10 systems for the period1950–1999, expressed in volumetric terms (m3m�3).Therefore, to visually compare GLDAS–NOAH10 andESMED soil moisture data, both time series were rescaledbetween 0 and 1 by normalizing them with the maximumand minimum value observed in the period. The obtainedtime series for all the locations are reported in Figure 3. TheR-values vary bymoderate to strong and range between 0.58and 0.90. The poorer matching is obtained for the two siteswith latitude higher than 48� in Germany and France likelydue to the influence of snow that is not accounted for in theESMED model. Table II summarizes the results of thecomparison between GLDAS–NOAH10 and ESMEDsoil moisture data in terms of R and RMSE. Bearing inmind the simplicity of the ESMED model and that theGLDAS–NOAH10 simulations are not used at all for themodel calibrations, the results can be consideredsatisfactory also for the temporal validation stage.In order to complete the validation with satellite data,

the same six locations were taken into consideration forcomparing data retrieval from remote sensing andESMED model. The results for the period 1979–2009are shown in Figure 4 by normalizing the data between0 and 1, note that satellite data before 1979 are notavailable. The R-values between soil moisture data fromESMED model and the SWI satellite system vary amongthe locations. An overall satisfactory correlation betweenthe two series was obtained, with the exception of theGerman site (Figure 4d, R =�0.11), likely due to thepresence of snow that significantly affects the reliabilityof the satellite soil moisture product. Otherwise, R-valuesvaried from 0.34 in the Sele Basin (Italy) up to 0.65 atSeville in Spain, thus showing an overall satisfactory

nnual soil moisture during 1950–2010 period obtained by the ESMEDmodel: a) Castilla, b) Batrage, c) Picardie, d) Friedberg, e) Sele Basin, and f) Seville.

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Figure 4. Comparison between the normalized (between 0 and 1) time series of annual soil moisture during 1979–2009 period obtained by the ESMEDmodel and the SWI-CCI satellite product for the six sites shown as black square in Figure 1 (same order of Figure 3)

Table II. Results of the comparison between the normalized (between 0 and 1) time series of annual soil moisture obtained by theESMED model, the GLDAS–NOAH system and the SWI-CCI satellite product for the six sites shown as black square in Figure 1

(Lat: Latitude, Long: Longitude, Elev: Elevation, R: Correlation coefficient, RMSE: Root mean square error (unitless))

GLDAS–NOAH (1950–2010) SWI-CCI (1979–2009) SWI-CCI (2000–2009)Site Lat (�) Long (�) Elev (m) R RMSE R RMSE R RMSE

Castilla (Spain) 40.2 �2.8 853 0.90 0.18 0.42 0.24 0.52 0.18Batrage (Serbia) 43.3 20.1 1150 0.70 0.18 0.63 0.21 0.75 0.19Picardie (France) 49.7 2.2 108 0.67 0.27 0.38 0.25 0.78 0.17Friedberg (Germany) 48.3 11.3 467 0.58 0.20 �0.11 0.38 0.56 0.36Sele Basin (Italy) 40.5 15.1 500 0.74 0.23 0.34 0.34 0.52 0.33Seville (Spain) 37.5 �6.5 100 0.85 0.14 0.65 0.22 0.68 0.19

MACRO-SCALE SOIL MOISTURE ESTIMATION IN THE EURO-MEDITERRANEAN REGION

agreement between the two datasets. Moreover, a betteragreement can be seen for themore recent years (since 2000)as the accuracy of the SWI product is expected to be higher(Liu et al., 2012). For instance, for Picardie site (France), theR-values are equal to 0.38 and 0.78 considering the periods1979–2009 and 2000–2009, respectively (similar findingsare obtained for all sites, see Table II).

CONCLUSIONS

Estimation of soil moisture in the root zone is importantfor improving hydrological modelling and forecasting,monitoring photosynthesis and plant growth and estimatingthe terrestrial carbon cycle. There are currentlyrelatively few global datasets on soil moisture, andhistorical dataset in reanalysis maps is not available

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before 1948 (Kalnay et al., 1996). Therefore, parsimonioushydro-climatological models can be useful to overcome thislimitation and address the estimation of long-term soilmoisture data very appealing for climatological studies. Thepresent study describes the development of a novel(parsimonious) model, ESMED, for soil moisture estima-tion that was successfully validated against independentmodelled datasets across the Euro-Mediterranean region(also supported by similar temporal patterns in ESMEDresults and satellite-based moisture data at selected sites).The ESMED model represents a simple and easy-to-use

tool, based on input observations freely available via webresources, which could be used for the reconstruction oflong-term historical soil moisture time series as well asto address future projections. Model evaluation againstin situ and satellite observations will be the object of

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future investigations to assess its capability to reproducesoil moisture spatial-temporal patterns also at finer scales.Thus, a transferable approach is recommended to applythe ESMED model at higher spatial (e.g. 10 km) andtemporal (monthly) resolution.

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