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In arid and semi-arid regions, irrigation management is important to avoid water loss by soil evaporation and deep percolation (DP). In this context, estimating the irrigation water demand has been investigated by many studies in the Haouz plain. However, DP losses beneath irrigated areas in the plain have not been quantified. To fill the gap, this study evaluated DP over two drip-irrigated citrus orchards (Agafay and Saada) using both water balance and direct fluxmeter measurement methods, and explored the simple FAO-56 approach to optimise irrigation in order to both avoid crop water stress and reduce DP losses in case of non-saline and saline soils. The experimental measurements determined different terms of the water balance by using an Eddy-Covariance system, fluxmeter, soil moisture sensors and a meteorological station. Using the water balance equation and fluxmeter measurements, results showed that about 37% and 45% of supplied water was lost by DP in Saada and Agafay sites, respectively. The main cause of DP losses was the mismatch between irrigation and the real crop water requirement. For Agafay site, it was found that increased over-irrigation had the effect of reducing soil salinity by leaching salts. The applied FAO-56 model suggested an optimal irrigation scheduling by taking into account both rainfall and soil salinity. The recommended irrigations could save about 39% of supplied water in non-saline soil at Saada and from 30% to 47% in saline soil at Agafay.

International audience; A meta-analysis data-driven approach is developed to represent the soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. The new model is tested across a bare soil database composed of more than 30 sites around the world, a clay fraction range of 0.02-0.56, a sand fraction range of 0.05-0.92, and about 30,000 acquisition times. SEE is modeled using a soil resistance ($r_{ss}$) formulation based on surface soil moisture ($\theta$) and two resistance parameters $r_{ss,ref}$ and $\theta_{efolding}$. The data-driven approach aims to express both parameters as a function of observable data including meteorological forcing, cut-off soil moisture value $\theta_{1/2}$ at which SEE=0.5, and first derivative of SEE at $\theta_{1/2}$, named $\Delta\theta_{1/2}^{-1}$. An analytical relationship between $(r_{ss,ref};\theta_{efolding})$ and $(\theta_{1/2};\Delta\theta_{1/2}^{-1})$ is first built by running a soil energy balance model for two extreme conditions with $r_{ss} = 0$ and $r_{ss}\sim\infty$ using meteorological forcing solely, and by approaching the middle point from the two (wet and dry) reference points. Two different methods are then investigated to estimate the pair $(\theta_{1/2} ; \Delta\theta_{1/2}^{-1})$ either from the time series of SEE and $\theta$ observations for a given site, or using the soil texture information for all sites. The first method is based on an algorithm specifically designed to accomodate for strongly nonlinear $\text{SEE}(\theta)$ relationships and potentially large random deviations of observed SEE from the mean observed $\text{SEE}(\theta)$. The second method parameterizes $\theta_{1/2}$ as a multi-linear regression of clay and sand percentages, and sets $\Delta\theta_{1/2}^{-1}$ to a constant mean value for all sites. The new model significantly outperformed the evaporation modules of ISBA (Interaction Sol-Biosph\`{e}re-Atmosph\`{e}re), H-TESSEL (Hydrology-Tiled ECMWF Scheme for Surface Exchange over Land), and CLM (Community Land Model). It has potential for integration in various land-surface schemes, and real calibration capabilities using combined thermal and microwave remote sensing data.

Data disaggregation (or downscaling) is becoming a recognized modeling framework to improve the spatial resolution of available surface soil moisture satellite products. However, depending on the quality of the scale change modeling and on the uncertainty in its input data, disaggregation may improve or degrade soil moisture information at high resolution. Hence, defining a relevant metric for evaluating such methodologies is crucial before disaggregated data can be eventually used in fine-scale studies. In this paper, a new metric, named G(DOWN), is proposed to assess the potential gain provided by disaggregation relative to the non-disaggregation case. The performance metric is tested during a four-year period by comparing 1-km resolution disaggregation based on physical and theoretical scale change (DISPATCH) data with the soil moisture measurements collected by six stations in central Morocco. DISPATCH data are obtained every 2-3 days from 40-km resolution SMOS (Soil Moisture Ocean Salinity) and 1-km resolution optical MODIS (Moderate Resolution Imaging Spectroradiometer) data. The correlation coefficient between G(DOWN) and the disaggregation gain in time series correlation, mean bias and bias in the slope of the linear fit ranges from 0.5 to 0.8. The new metric is found to be a good indicator of the overall performance of DISPATCH. Especially, the sign of G(DOWN) (positive in the case of effective disaggregation and negative in the opposite case) is independent of the uncertainties in SMOS data and of the representativeness of localized in situ measurements at the downscaling (1 km) resolution. In contrast, the traditional root mean square difference between disaggregation output and in situ measurements is poorly correlated (correlation coefficient of about 0.0) with the disaggregation gain in terms of both time series correlation and bias in the slope of the linear fit. The G(DOWN) approach is generic and thus could help test a range of downscaling methods dedicated to soil moisture and to other geophysical variables.

Due to their image-based nature, "contextual" approaches are very attractive to estimate evapotranspiration (ET) from remotely-sensed land surface temperature (LST) data. Their application is however limited to highly heterogeneous areas where the soil and vegetation temperature endmembers (Tends) can be observed at the thermal sensor resolution. This paper aims to develop a simple theoretical approach to estimate Tends independently from LST images. Soil Tends are simulated by a soil energy balance model forced by meteorological data. Vegetation Tends are obtained from soil Tends and air temperature. Model-derived soil Tends are first evaluated with in situ measurements made over an irrigated area in Morocco. The root mean square difference (RMSD) between modeled and ground-based soil Tends is estimated as 2.4 [GRAPHICS] C. Model-derived soil Tends are next compared with the soil Tends retrieved from 90-m resolution ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data collected over two irrigated areas in Mexico and Spain. Such a comparison reveals a strong consistency between model-derived and high-resolution image-based soil Tends. A recent contextual ET model (SEB-1S) is then applied to 90-m resolution and to 1-km resolution (aggregated) ASTER data using the model-derived or image-based Tends as the input. The RMSD between 90-m resolution SEB-1S and in situ ET is estimated as 65 and 82 W center dot m [GRAPHICS] , and the RMSD between 1-km resolution SEB-1S and aggregated SEB-1S ET is estimated as 78 and 56 W center dot m [GRAPHICS] for the image-based and model-derived Tends, respectively. In light of the above results, Tends should be estimated a priori when contextual models are applied to low resolution images. Moreover, the consistency over highly heterogeneous areas between model-derived and high-resolution image-based Tends provides a meaningful basis for developing mixed modeling observational approaches.

In arid and semi-arid regions water scarcity is one of the main limiting factors for economic growth. In the context, an experimental setup was conceived to monitor seasonal water consumption of citrus plant irrigated by drip irrigation system in Agafay station, middle of Morocco. For that, an Eddy-Covariance system, meteorological station, fluxmeter, as well as measurements of soil moisture and temperature were continuously operated during experimentation. The stable isotope was used to partition Evapotranspiration (ET) components. By using the water balance equation, the results showed that about 37% of total irrigation and rainfall is lost by infiltration and runoff. Additionally, the partitioning of evapotranspiration using the stable isotope showed that soil evaporation is very small compared to the transpiration at least during tow sampling days. This result confirms that the irrigation method applied by the farmer was very appropriate for the orchard but it is necessary to re-examine amount of water applied and timing of irrigation in order to minimise the loss by infiltration

This study presents the results of an evaluation of two indirect methods (Plant Canopy Analyser (PCA) and hemispherical photographs) for measuring vegetation area index VAI of two orange orchards, differing by their ground fraction cover (fc). The evaluation is based on reference data obtained by destructive measurements on limited samples, which allow to calibrate and validate an exponential relationship between the diameter of branches and associated areas of leaves (R2 = 0.99). The obtained results show that the two indirect methods underestimate the reference values of VAI. For the PCA device, the best estimates of VAI are obtained using the five rings for high fc (fc = 0.7) and using only four rings for low fc (fc = 0.3). For both case, the hemispherical photographs give accurate estimates of VAI: the relative errors are about 11 and 14% for high and low fc, respectively. Alternatively, a simple method consisting of calculating VAI as the weighted average of the maximum (VAImax, below the tree) and minimum (VAImin, at the center of four trees) values using fc as a weighting factor, was successfully tested (R2 = 0.90). For both indirect methods, the tree volumes are well estimated with comparison to the values calculated assuming an ellipsoidal form.

International audience

International audience