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Technical Note

Developing and testing a long-term soil moisture dataset at the catchment scale

L. Brocca a,⇑, G. Zucco a, T. Moramarco a, R. Morbidelli b

a Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy b Department of Civil and Environmental Engineering, Perugia University, Via Duranti 93, 06125 Perugia, Italy

a r t i c l e i n f o

Article history:Received 18 December 2012 Received in revised form 18 March 2013 Accepted 22 March 2013 Available online 4 April 2013 This manuscript was handled by Peter K.Kitanidis, Editor-in-Chief, with the assistance of J. Simunek, Associate Editor

Keywords:Soil moisture Soil water balance model Long-term dataset Hydrological applications Satellite soil moisture validation

s u m m a r y

Inferring long-term soil moisture time series with dense temporal resolution and representative of large areas is a challenging task. However, its accurate estimation over large areas might be essent ial for improvin g our knowledge of the mass and energy balance between the land surface and atmosphere,and also for many others practical applications. In this study, a long-term (1989–2011) simulated soil moisture dataset is developed by using 1-year in situ observ ations collected at 92 sites over an area of�400 km2 in central Italy. Specifically, a soil water balance model is calibrated for reproducing the soil moisture temporal variability at each site and, then, is tested for representing also the spatial variability of in situ measurements. The good temporal and spatial agreement between modelled and observed data gives confidence about the use of the modelled data in the study area for reconstructing a long-term soil moisture dataset with hourly temporal resolution (in accordance with the availability of hydrometeoro- logical observations). The developed soil water balance model and procedure can be applied also in other climatic regions to obtain a similar dataset. Indeed, the obtained dataset and the model code are made freely available from the authors and can be used for hydrological and satellite soil moisture products validation studies.

� 2013 Elsevier B.V. All rights reserved.

1. Introductio n

Surface soil moisture is a key variable (Seneviratne et al., 2010 )for hydrological , meteorologi cal, agronomic and climatic studies.Recent works have shown the significant role of soil moisture for flood forecasting (Chen et al., 2011; Brocca et al., 2012a ), shallow landslides triggering (Bittelli et al., 2012; Brocca et al., 2012b ),numerical weather prediction (Dharssi et al., 2011; de Rosnay et al., in press ) and climate modelling (Dorigo et al., 2012a ). There- fore, in the last decade, scientific communi ty has been making a big effort for addressing the estimation of soil moisture over large areas with good spatial (10–100 km2) and temporal (0.5–1 day)resolution through in situ sensors (Robinson et al., 2008; Dorigo et al., 2011 ), remote sensing (Wagner et al., 2007; Kerr et al.,2012) and modelling approaches (Koster et al., 2009; Albergel et al., 2012 ).

Concerning in situ measureme nts, long-term soil moisture time series are quite rare due to frequent issues that usually occur (e.g.change in the calibration curve, battery or transmission failure,sensor malfunctioning) and also the interrupti on of monitoring

programs due to a lack of budget, personnel, interest, etc. (Dorigoet al., 2012b ). Indeed, soil moisture campaigns are usually carried out for long periods of time, but on limited areas (De Lannoy et al., 2007; Hu et al., 2010 ) or for large areas, but for a short time period (Jacobs et al., 2004; Choi and Jacobs, 2007, 2010; Famigliet tiet al., 2008 ). Only recently, specific soil moisture monitoring net- works have been set up for investigating long-term (>2–3 years)and large-scale (>100 km2) soil moisture temporal variability.Some examples are in the United States (Li and Rodell, 2012; Bell et al., in press ), France (Albergel et al., 2009 ), Spain (Martinez- Fer- nandez and Ceballos, 2005 ), Switzerland (Mittelbach and Sene- viratne, 2012 ) and Australia (Smith et al., 2012 ).

This study aims at developing a long-term soil moisture dataset through the integration of in situ measureme nts (Brocca et al.,2012c) and a soil water balance model (Brocca et al., 2008, inpress-a). Specifically, the soil water balance model is calibrate dand tested for simulating not only the temporal variability of soil moisture measurements , but also their spatial variability in two mesoscale study areas (178 and 242 km2) in central Italy. Weekly soil moisture field campaigns were carried out at 46 sites during a 1-year period, thus obtaining 34 sampling days. Afterward, the model is applied over a long-term period (1989–2011) to develop a simulated soil moisture dataset that can be used for practical applicati ons and for the validation of satellite soil moisture retri- evals (Crow et al., 2012 ) in the study area. The ERA-Land reanalysis

0022-1694/$ - see front matter � 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.jhydrol.2013.03.029

⇑ Corresponding author. Address: Research Institute for Geo-Hydrological Pro- tection, National Research Council, Via Madonna Alta 126, 06128 Perugia, Italy. Tel.:+39 0755014418; fax: +39 0755014420.

E-mail address: luca.brocca@irpi.cnr.it (L. Brocca).

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soil moisture dataset (Balsamo et al., 2012; Albergel et al., in press )produced by ECMWF (European Centre for Medium-Range Weath- er Forecasts) is also compare d with the simulated soil moisture dataset. It is worth underlining that the dataset and the soil water balance model are made freely available from the following website: http://hydro logy.irpi.cnr.it/ people/l.brocca .

2. Soil water balance model

Large-sca le soil moisture spatial and temporal variability issimulated through a parsimoniou s lumped soil water balanced model is applied. The structure of the model used in this study was develope d by Brocca et al. (2008) and was widely tested and applied in different test-sites located across Europe obtaining satisfying performance levels (e.g. Brocca et al., 2008; Brocca et al., 2011a; Brocca et al., in press-a; Brocca et al., in press-b;Lacava et al., 2012; Penna et al., 2013 ).

The model simulates the soil moisture content for a soil layer for which the following water content balance equation holds:

Z dhðtÞdt ¼ f ðtÞ � eðtÞ � gðtÞ hðtÞ < hs

hðtÞ ¼ hs hðtÞP hs

(ð1Þ

where h(t) is the average soil moisture (in volumet ric terms, % vol/ vol) of the investig ated soil layer, Z is the soil layer depth, t is the time, hs is the soil moisture value at saturation, and f(t), e(t) and g(t) are the infiltration, evapotran spiration and percolat ion rates,respective ly.

The model requires as input data two variables that are routinely measured (rainfall and air temperature) and simulates the soil moisture temporal evolution at point scale. The complete formula- tion of the model can be found in Brocca et al. (2008 (in press-a) andPonziani et al. (2012). Only a brief description of the different components is given here. The infiltration rate f(t) is estimated byusing the Green-Ampt equation; the drainage rate g(t) isrepresented by a non-linea r relationship able to simulate the steep falling limb that occurs immedia tely after a rainfall event. The

Blaney and Criddle formulation is adopted to estimate the potential evapotransp iration (Doorenbos and Pruitt, 1977 ); the actual evapo- transpira tion rate e(t) is a fraction of the potential one accordin g tothe degree of saturation of the soil layer, he ¼ ðh� hrÞ=ðhs � hrÞ, with hr the residual soil moisture value.

The values of seven paramete rs need to be assigned: Z, hs, hr, Kc

(correction factor for evapotrans piration), Ks (saturated hydraulic conductivity ), wb (wetting front soil suction head), and k (pore size distribut ion index). To reduce the model uncertainty related to the need of estimating a large number of parameters (Beven, 2008 ), aprocedure for limiting the number of paramete rs is adopted.Specifically, the values of hs and hr are simply derived from the maximum and the minimum observed soil moisture values,respectively , while Z is fixed considering the measureme nt depth of in situ sensors. Similarly, the factor Kc is set to a constant inaccordance with previous modelling applications carried out inthe same geographic regions (Brocca et al., 2008 ). For the three remaining parameters, Ks, wb and k, we choose to calibrate only the saturated hydraulic conductivi ty, Ks, and to estimate the remaining two as a function of Ks by using the following empirical fitting relationshi ps accordin g to the experimental values obtained for different soils by Rawls et al. (1982):

wb ¼ 54:727 log ðKsÞ � 323:9

k ¼ 0:085 log ðKsÞ þ 0:1574

with Ks expressed in mm/h and wb in mm.

3. Study area and dataset

Soil moisture measurements were carried out on two areas lo- cated within the Umbria Region (central Italy): the basin of the Trasimeno Lake and the basins of Genna and Caina streams, which will subsequently be referred to as LAGO and GENCAI, respectively (see Fig. 1). LAGO is located around the largest body of water in the Tiber Valley, the Trasimeno Lake, and covers an area of 178 km2,with a mean slope of 5%; the predominant land use is agricultural

Fig. 1. Location of the soil moisture sampling sites and of the hydro-meteorological network for the two study areas: Trasimeno Lake (LAGO) on the left and Genna-Caina basins (GENCAI) on the right.

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(70%), followed by forested areas (15%). GENCAI covers an area of242 km2, the predominant land use is agricultural (73%), followed by urban areas (12%) and pasture (15%), the slope is slightly greater than that of the LAGO, with an average value of 9%. The region ischaracterise d by a semi-hum id Mediterrane an climate, with anaverage annual rainfall of �900 mm, occurring mostly in autumn and spring; the annual average temperature is 12 �C. Table 1 sum-marises the main characteristics for both study areas.

Soil moisture measure ments were carried out by using a porta- ble Time Domain Reflectometer (TDR) with two 15 cm-long wave- guides spaced 5 cm. Therefore, the sampled volume of soil is equal to �0.1–1 dm3 (Robinson et al., 2008 ) and the sensor was removed after each measureme nt. The standard calibration curve (Skaling,1992) is used to obtain volumetric soil moisture values from the measured dielectric constant by the TDR. The sampling design adopted is the same for both areas of study: 46 sites were identi- fied (one site each 4–5 km2) and, for each one, three point mea- surements were randomly made within an area of �5 m2

diameter of 1 m during each sampling day between 09:00 and 12:00 (local time) in the morning (Brocca et al., 2012c ). Measure- ments were repeated from February 2009 to January 2010 with afrequency almost weekly with the exception of the summer months because of the hardness of the ground, which did not allow the survey. During the investigated period, measurements were carried out in the two areas during nearly the same days to obtain 34 sampling days. Fig. 1 shows the topography of the two study areas, the location of the soil moisture measure ments and of the hydro-meteor ological network.

To further testing and analysing the long-term simulated soil moisture dataset, the ERA-Land soil moisture produced by ECMWF is also employed (Balsamo et al., 2012; Albergel et al., in press ).ERA-Land is a newly developed land product generated at ECMWF that benefits from the most recent land modelling improvements.In particular, the H-TESSEL Land Surface Model (LSM), with respect to the TESSEL scheme used for the ERA Interim reanalysis dataset,is adopted. The improvements in the H-TESSEL LSM scheme, inparticular with respect to soil moisture (Albergel et al., 2012 ),provided the motivatio n for producing an updated land surface reanalysis using offline (land-only) LSM simulations . In fact, the ERA-Interim near-surface meteorologi cal forcing has been used to force the improved land surface scheme (Balsamo et al., 2012 ).The soil moisture product has a spatial resolution of about 80 km(T255) with analyses available for 00:00, 06:00, 12:00 and 18:00 UTC; it covers the period 1 January 1979 to 31 December 2010.The soil moisture product considers four layers of soil (0–7,7–28, 28–100 and 100–289 cm). In this study, the first two soil lay- ers are used to obtain a surface (0–7 cm) and a root-zone (0–28 cm) soil moisture product.

4. Results

The results of the soil water balance model simulations are sub- divided in two parts: firstly, the capability of the model to repro- duce the temporal variability at point scale is assessed and,secondly, the agreement between observed and modelled spatial variability is investigated. Finally, the simulated long-term soil moisture dataset is described and compared with the ERA-Land reanalysis soil moisture dataset produced by ECMWF.

4.1. Simulation of point scale temporal evolution

The first step for the application of the soil water balance model in the two areas consists in the selection of the parameters that are not involved in the calibration. As mentioned above, the value ofthe saturated (residual) soil moisture is simply taken by visually identifyin g the maximum (minimum) soil moisture observed value, slightly increased (decreased) of +5% (�2%) to take into account that extreme soil moisture values might not have been measure d in the investiga ted period, mainly saturated condition sas measurements during rainfall events were not taken. The soil layer depth is evaluated by increasing to some extent the soil volume monitored by the employed TDR, therefore it is set to25 cm. Finally, for the estimation of Kc, a value equal to 1.1 is fixedconsideri ng previous soil moisture simulatio ns successfully con- ducted close to the investiga ted area (Brocca et al., 2008 ).

For each site in the two areas, the rainfall and temperature data were interpolated through the inverse distance weighted method to obtain the forcing observati on of the soil water balance model.So, based on the whole dataset of in situ observati ons, the param- eter Ks was calibrate d, separately, for each site. Specifically, a split- sample test, i.e. the splitting of the observation period in two part,one for paramete rs calibration and one for model validation, is not considered here as the main purpose is to develop a simulated soil moisture dataset that matches as much as possible the observa- tions. The maximisation of the Nash–Sutcliffe, NS, efficiency index is selected as objective function; the Root Mean Square Error,RMSE, and the correlation coefficient, r, are also selected for model evaluation. A summary of performanc es is given in Table 2 in terms of 20th, 50th and 80th percentiles of NS, r and RMSE. Overall, the results are satisfacto ry with median NS equal to 0.734 and 0.614 for LAGO and GENCAI areas, respectively. Moreover, the model performanc e is satisfacto ry for all sites as highlight ed by the high NS-value s correspondi ng to 20th percentile (0.658 and 0.532 for LAGO and GENCAI areas). In terms of RMSE, median values are between 4.11 and 4.94% vol/vol, slightly higher than those ob- tained in previous model applicati ons (Brocca et al., 2008, inpress-a,b ; Lacava et al., 2012 ). This can be ascribed to the different methods used to obtain in situ measurements . Indeed, in this study we used spot measure ments that could not be repeated exactly inthe same position from date to date; in previous studies (Broccaet al., 2008, in press-a,b ; Lacava et al., 2012 ) continuous soil mois- ture observations were employed thus assuring that measure -ments are always representat ive of a specific point location.Therefore, the measure ments carried out in this study are affected by higher uncertainty (see also below in the ‘‘Spatial analysis’’ sec- tion) that might reduce the model performance. Concerning the model parameter Ks, it was found quite variable from site to site with values in the range 0.29–7.65 mm/h and 2.52–24.78 mm/h for LAGO and GENCAI areas, respectively . The spatial coefficientof variation of Ks-values is around 62% for both areas. The high var- iability of Ks is in accordance with previous studies (Nielsen et al.,1973; Sharma et al., 1980; Jones and Wagenet, 1984 ), also made inthe same study area (e.g. Corradini et al., 1998 ) and depends by the model type (conceptual) adopted for the mathematical representa- tion of the local infiltration process.

Figs. 2 and 3 show the temporal patterns of observed and mod- elled data for three randomly selected sites in the two study areas and for the average of all sites (Figs. 2 and 3d). As it can be seen, the

Table 1Main characteri stics of the investigated areas and of the soil moisture sampling campaigns.

Basin Extent (km2) Period of measurement Number of sampling days Number of sampling sites

LAGO 178 February 2009 – January 2010 34 46GENCAI 242 February 2009 – December 2009 34 46

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Table 2Summary of the performance of the soil water balance model for estima ting the temporal evolution of soil moisture at point scale for LAGO and GENCAI areas (NS: Nash–Sutcliffeefficiency index, r: correlation coefficient, RMSE: Root Mean Square Error).

Basin NS percentiles r Percentiles RMSE percentiles (% vol/vol)

20� 50� 80� 20� 50� 80� 20� 50� 80�

LAGO 0.658 0.734 0.819 0.825 0.871 0.915 3.31 4.11 5.00 GENCAI 0.532 0.614 0.720 0.750 0.794 0.853 3.93 4.94 5.85

Fig. 2. Simulated (SIM) and observed (OBS) soil moisture temporal pattern in the LAGO area at three randomly selected sites (see Fig. 1 for the location): (a) 3, (b) 17, (c) 36,and (d) the spatial average values of the whole area (NS: Nash–Sutcliffe efficiency index, RMSE: Root Mean Square Error).

Fig. 3. As in Fig. 2 but for the GENCAI area at sites: (a) 11, (b) 25, (c) 39, and (d) the spatial average values of the whole area (NS: Nash–Sutcliffe efficiency index, RMSE: Root Mean Square Error).

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model, besides to be able to follow the seasonal evolution of the observed soil moisture, well represents the short-term variability linked to the single rainfall events. Specifically, the best perfor- mance is obtained when the spatial average over all sites isanalysed with NS-value s equal to 0.923 and 0.821 for LAGO and GENCAI, respectively . Indeed, by averaging the observati ons, the influence of the random errors due to measure ment noise and the employed sampling scheme is strongly reduced.

4.2. Simulation of soil moisture spatial variability

In addition to the temporal stability analysis, the capability ofthe model to reproduce the spatial variability of soil moisture among sites is tested. Specifically, as usually made in studies inves- tigating soil moisture spatial variability (Famiglietti et al., 2008;Penna et al., 2009; Brocca et al., 2012c; Gao et al., 2013 ), the rela- tionships between the spatial mean soil moisture and the corre- sponding standard deviation and coefficient of variation are firstly analysed. Specifically, the hourly simulated dataset is resam- pled every 3 h (for sake of simplicity of visualisation) and the spatial statistics are computed. Fig. 4 shows the relationship s be- tween mean and standard deviation and coefficient of variation for simulated and observed data in the two study areas. It is clear that simulated data tends to underest imate the observed soil mois- ture spatial variability even though the general patterns between the statistics are similar. Indeed, a convex upward relationship between mean and standard deviation and an exponential decrease of coefficient of variation with mean can be detected (Choi and Jacobs, 2007; Famiglietti et al., 2008; Penna et al.,2009; Li and Rodell, 2012 ).

A possible explanation for the underestimation of the spatial variability of simulated data can be ascribed to the role of mea- surement error that is not taken into account in the simulated data.It can be expected that the spatial variability of observed data isartificially increased by a random error related to the

measure ment noise and the non-exact co-location of the different measure ments carried out in different dates. This error is modelled here by adding to the simulated data a random error following aGaussian distribution with mean equal to zero and standard devi- ation of 3% vol/vol. The latter value is obtained by increasing the quoted error of TDR method (±2%; Skaling, 1992 ) for the additional error due to the measurement scheme. These new simulated data- sets are reported in Fig. 4 where it can be seen that the magnitude of spatial variability of observed and simulated data is the same.On the other hand, by considering statistics of a higher order (skewness and kurtosis), the range of modelled and observed data is similar (not shown for brevity) and is not affected by the addi- tion of the random error.

Additional analysis is carried out by computin g the matrix ofspatial correlation between the different dates for the simulated data. For each date (every 3 h), the spatial cross-correlation with the preceding (and following ) dates is computed and shown inFig. 5. It can be seen that during the winter and the summer months, for which soil moisture is characterise d by almost con- stant values, correlations are quite high (>0.8) also for sampling sseparated by several weeks. More interestingly, the samplings car- ried out in winter are significantly correlated even in the case that they are separated by more than 12 months. This can be seen inFig. 5 by consideri ng, for instance, the high r-values obtained for both areas between the measure ments carried out in January 2009 and 2010. On the contrary, in the transition period between dry and wet conditions (or viceversa) the correlation values strongly decrease (<0.4), especiall y when changing from dry towet conditions. These results are in accordance with previous stud- ies (e.g. Martinez-Fe rnandez and Ceballos, 2005; Brocca et al.,2012c). Moreover, by comparing the cross-correlati on matrix with the observed one (Fig. 6 in Brocca et al., 2012c ), it can be observed that simulated data show quite higher correlation values but this can be expected for the influence of the random error as shown before. Additionally, GENCAI area shows higher correlations with

Fig. 4. Relationship between the mean soil moisture and (a and b) the standard deviation, (c and d) the coefficient of variation for observed, OBS, and simulated, SIM, data for:(a and c) LAGO, and (b and d) GENCAI area. The relationship for the simulated data for which a random error, Gaussian with mean zero and standard deviation of 3% vol/vol, isadded, SIM �, are also shown.

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respect to LAGO and this also agrees with the observed cross- correlation matrices (Brocca et al., 2012c ).

4.3. Long-term soil moisture dataset

Based on the encouraging results obtained through the soil water balance model, a long-term soil moisture dataset is created by forcing the model with rainfall and temperature data from 1989 to 2011. As described before, the hydrometeoro logical data are interpolated for each site and, by using the calibrated parame- ter dataset, the model is simply run for the whole period. More- over, bearing in mind that for the validation of satellite soil moisture products a near surface soil moisture value is needed,the model is also run reducing the layer depth Z to 5 cm.

Fig. 6 shows, as an example, the daily simulated soil moisture time series (averaged on the 46 sites), for 25 and 5 cm layer depths,for the period 2004–2011 for the LAGO area. The spatial variability between sites is also displayed consideri ng, by way of example, ±1the spatial standard deviation . The seasonal soil moisture pattern is clearly observed with dry conditions in summer and wet condi- tions in winter. Moreove r, the particular dry condition s in the area

that occurred in 2007 and 2011 can be depicted as well as the very wet periods in November–December 2005 and January 2010 that produced widespread flooding (see e.g. Brocca et al., 2011b ). When comparing the time series at the two sampling depths, one can observe the significantly higher temporal variability (higher fluctu-ations) and the higher spatial variability (larger grey areas) at 5 cmcompare d to 25 cm. This result is expected as a thinner layer depth that is closer to the soil surface is more influenced by meteorolog- ical forcings (rainfall and evapotransp iration), thus showing higher spatial–temporal variability.

For further testing the long-term simulated soil moisture data- set, it is compared with the ERA-Land soil moisture product for the two study areas. Fig. 7 shows the comparis on for the GENCAI area in the period 1980–2010 and for the surface (�5 cm) and root-zone (�25 cm) soil layers. As it can be seen, a good temporal agreement between the two dataset is detectable even though the minimum values of the simulated data is slightly lower than the ERA-Land soil moisture product (the same occurs also for the LAGO area).By using the whole period (1979–2011), the r-values for the surface (root-zone) layer between the two datasets are equal to0.803 (0.879) and 0.782 (0.864) for LAGO and GENCAI, respectively.

Fig. 5. Matrix of the spatial correlation coefficients between simulated soil moisture data on different dates for: (a) LAGO, and (b) GENCAI area.

Fig. 6. Long-term daily soil moisture time series, for layer depths of 5 and 25 cm, obtained through the application of the soil water balance model and averaged for all (46)the sites in the LAGO area; the grey area represents ±1 spatial standard deviation of values. The daily rainfall data are also shown in the upper panel.

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Therefore, even though this cannot be considered a strict validation of the simulated data as the reanalysis soil moisture product does not represent an observati on, the agreement between the datasets confirms the reliability of the developed procedure and, at the same time, the good performanc e of the ERA-Land soil moisture product. As the latter is available at global scale, it is expected that it could be effectively used for continental scale studies. Although similar results are inferred, the higher spatial resolution of the developed soil moisture dataset shows the benefit to use the proposed approach .

Moreover, by analysing the simulated long-term soil moisture dataset, it can be seen that the minimum soil moisture value at25 cm depth in the GENCAI area, observed in 2001 (�10% vol/ vol), is not significantly different from the one observed in 2009 (11.6% vol/vol) when soil moisture data were collected. As regards the maximum soil moisture values (�45% vol/vol), they were reached several times in the study period, and also in December of 2009. Therefore, the maximum and minimum soil moisture val- ues observed in the observation period (February 2009 – January 2010) nearly cover the whole range of soil moisture condition s inthe period 1989–2011. Similar findings (not shown) are also observed for LAGO area.

5. Conclusion s

In this study a long-term soil moisture dataset is developed byusing a soil water balance model calibrate d on a 1-year field

campaign carried out in central Italy (�400 km2). The model isfound to be able to reproduce both the temporal (Figs. 2 and 3)and spatial (Figs. 4 and 5) variability of in situ observations with good accuracy. Moreover, a good agreement between the gener- ated long-term (1989–2011) hourly time series and the ERA-Land reanalysi s soil moisture dataset produced by ECMWF is observed (Fig. 7). The benefit in using the developed dataset with respect to the existing ones, such as the ERA-Land product, consists in its higher spatial and temporal resolution. Therefore, the modelled dataset can be considered reliable in estimating soil moisture spa- tial–temporal evolution in the area. For that, the long-term hourly time series, along with the code of the soil water balance model,are made freely available to the scientific communi ty (see athttp://hyd rology.irpi.cnr.it/people/l.broc ca). In fact, it is expected that this dataset could represent a valuable benchma rk for the validation of coarse resolution satellite soil moisture products.Moreove r, the use of these data within hydrologi cal modelling for improving runoff prediction can be tested. Finally, we note that the developed soil water balance model and procedure can beeasily applied over different climatic regions for developing long- term and continuo us soil moisture time series representat ive oflarge areas.

Acknowled gments

The authors wish to thank R. Rosi for his assistance. This work was funded by the National Research Council of Italy and by the

Fig. 7. Comparison between the long-term daily soil moisture time series (averaged on the 46 sites) and the ERA-Land soil moisture product in the 20-year period 1990–2010for the GENCAI area: (a) surface layer �5 cm, and (b) root-zone layer �25 cm.

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Italian Ministry of Education, University and Research (specialProject ‘Assimilazione di osservaz ioni remote e al suolo per lacalibrazione di modelli idrologic i distribuiti e la prevision e delle piene improvvi se’). We are also grateful to the three anonymous reviewers and the Associate Editor for their comments and sugges- tions that helped to improve an earlier version of the manuscript.

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