François Hupet

Intraseasonal dynamics of soil moisture variability within a small agricultural maize cropped field

The spatio-temporal dynamics of soil water content was investigated within a small agricultural maize cropped field located in Belgium. Soil moisture measurements were intensively, made between May, 30 and September 13. 1999 on 28 sampling locations at different depths (from 0 to 125 cm) with both TDR and neutron probe. The adopted sampling scheme resulted in a comprehensive data set of nearly 8000 soil moisture measurements. Using this data set, we first probe the role of factors controlling the spatio-temporal soil moisture dynamics for the different considered soil depth. Special emphasis is thereby given to the role of the vegetation in the space-time relationships of soil Moisture. Secondly, we identify the temporal dynamics of the spatial structure of soil moisture pattern at different soil depth. Thirdly, we investigate the relationship between the mean soil moisture and the spatial variability across time, analysing through the season the optimal sampling strategies to adopt for providing the field areal soil moisture within a given predefined error limit. The result showed that the spatially variable within the held and subsequently through the process of evapotranspiration and the root Water Uptake, plays a non-negligible role in the temporal dynamics of the observed soil Moisture patterns for the superficial layers. The spatial structure of these soil moisture patterns,vas non-existent or only weakly marked. The study finally indicated that a negative correlation exists between the spatial variability and the mean soil moisture, implicitly suggesting that the sampling has to be more intensive for the drier conditions. Besides these results, this study reemphasises the importance of conducting, soil moisture spatial variability studies with measurements performed on the entire hydrological active zone and to adopt temporally unchanged sampling locations in order to progress in the thorough understanding of the physical processes generating the soil moisture spatial variability

A global multilevel coordinate search procedure for estimating the unsaturated soil hydraulic properties

We present a new inverse modeling procedure to characterize the hydraulic properties of partially saturated soils from soil moisture measurements during a natural transient flow experiment. The inversion of the governing one-dimensional Richards equation is carried out using the Global Multilevel Coordinate Search optimization algorithm in sequential combination with the local Nelder-Mead Simplex algorithm (GMCS-NMS). We introduce this optimization method in the area of unsaturated zone hydrology since it is adapted for solving accurately and efficiently complex nonlinear problems. Several numerical experiments have been conducted to evaluate the proposed inversion method using synthetic error-free and error-contaminated data for different textured soils. Inversion of the simulated error-free data and examination of the related response surfaces demonstrated the uniqueness of the inverse solution and the suitability of the GMCS-NMS strategy when identifying four key parameters of the hydraulic functions described by the Mualem-van Genuchten model. Inversion of the error-contaminated data proved further the good stability of the inverse solution that is consistent with the needs required by real experiments.

Effect of the sampling frequency of meteorological variables on the estimation of the reference evapotranspiration

In this paper, we quantify the effect of the temporal sampling frequency of commonly measured climatic variables on the estimation of the reference evapotranspiration. Using a set of data sampled on an intensive basis (i.e. one measurement each minute) during a period of 6 months, we first analyse the effect of the temporal sampling frequency on the estimation of the daily means of the shortwave solar radiation, the wind speed, the dry and wet temperatures, and on the estimation of the daily maximum and minimum dry temperature. Subsequently, a sensitivity analysis of a reference evapotranspiration model is carried out to determine the most sensible meteorological variables. The sensitivity coefficients were then combined with the errors due to the temporal sampling to quantify for each variable the impact of the sampling frequency on the estimation of daily ETo. The results showed that the solar radiation and the wind speed are the most sensitive to bias induced by inadequate temporal sampling frequency. Daily errors of 5.1 MJ m(-2) d(-1) or 41.05% for the solar radiation, and 0.45 m s(-1) or 18% for the wind speed may be obtained if these variables are inappropriately sampled. Moreover, the impact of inappropriate temporal sampling on the estimation of ETo can be significant with respective maximum bias of 0.61 mm d(-1) due to inappropriate solar radiation sampling and 0.36 mm d(-1) due to inappropriate maximum temperature sampling. A non-intensive hourly temporal sampling schedule of all meteorological variables may induce errors on the daily ETo so high as -0.76 mm d(-1) or -27%. Fortunately, the errors generated on the estimation of the long-term integrated evapotranspiration are clearly lower (3.8%). Our study clearly demonstrates the importance of scheduling appropriately the sampling frequency of climatic variables to correctly estimate land surfaces fluxes as well in fundamental as in more practically oriented research studies

Impact of plant water uptake strategy on soil moisture and evapotranspiration dynamics during drydown

Experiments have shown that plants can compensate for water stress in the upper, more densely rooted, soil layers by increasing the water uptake from deeper layers. By adapting root water uptake to water availability, plants are able to extend the period of unstressed transpiration. This strategy conflicts with the approach in many land surface schemes, where plant water uptake is treated as a static process. Here we derive expressions for the typical drydown trajectories of evapotranspiration and soil moisture for both strategies. We show that the maximum difference in evapotranspiration between the two strategies during drydown can exceed 50%. This in turn leads to a difference in root zone soil moisture of up to 25%. The results stress the importance of incorporating realistic root water uptake concepts in land surface schemes.

On the identification of macroscopic root water uptake parameters from soil water content observations

[1] In this paper, we analyze the identification problem of macroscopic root water uptake parameters from soil water content observations. For this study, the macroscopic root water uptake is considered to be linearly decreasing with depth with A and B parameters conditioning, respectively, the maximum root water uptake at the surface and the decreasing rate with depth. For identification of parameter A and B, two different identification approaches are tested using a detailed soil moisture data set consisting of vertical profiles measured with time domain reflectometry (TDR) and neutron probe on 28 locations within a small maize cropped field during a dry period of 26 days. The first approach is based on a simplified water balance, while the second one uses an integrated soil-vegetation-atmosphere transfer (SVAT) model in an inverse mode. Results of the simplified water balance show first that the root water uptake measured within the field is quite variable with CV ranging from 22 to 34% for the root uptake parameters A and B, respectively. Furthermore, positive correlation between A and B suggests that low superficial root water uptake could be compensated by high deep root water uptake. Second, numerical simulations used to test the validity of this simplified approach show that the method is quite robust, at least for a certain range of A parameter values (0.008 < A < 0.013), for B, and for all investigated differently textured soils, except the coarse sand. To investigate the feasibility and the robustness of the second approach, we address the problems of insensitivity, instability, and nonuniqueness by means of numerical simulations. Results show, for soils with different textures, that the soil water content and, to a lesser extent, the time derivative of soil water content are quite insensitive to root water uptake parameters, at least compared to soil hydraulic parameters. Furthermore, instability analysis clearly illustrates that root water uptake parameters are quite and even very instable with respect to small uncertainties on the apparently known parameters (e. g., soil parameters). Finally, results show that if root water uptake plus additional other parameters (e. g., soil parameters) are simultaneously optimized, nonuniqueness of parameter sets must be expected. Therefore a robust identification of root water uptake parameters by means of a full SVAT inversion is unlikely to be achieved during a dry period with soil water content observations at least when some other parameters driving the system, like soil hydraulic properties, are not error free. In this context, further in depth research is needed to investigate if the use of longer periods characterized by both drying and redistribution events may increase the success of a full SVAT inversion. Finally, to extend the results of this study, we recommend applying a similar analysis to other macroscopic conceptual root water uptake models.

Laboratory evaluation of a hydrodynamic inverse modeling method based on water content data

The inverse modeling method of Lambot et al. [2002] for estimating the hydraulic properties of partially saturated soils, which was numerically validated, is further tested on laboratory-scale transient flow experiments. The method uses the global multilevel coordinate search algorithm combined sequentially with the local Nelder-Mead simplex algorithm to obtain the inverse of the one-dimensional Richards equation using soil moisture time series measured at three different depths during natural infiltration. Flow experiments were conducted on a homogeneous artificial sand column and three undisturbed soil columns collected from agricultural fields. Three models describing the unsaturated soil hydraulic properties were used and compared: the model of Mualem and van Genuchten, the model of Assouline, and the decoupled van Genuchten-Brooks and Corey combination. The performances of all three models were similar, except for Assoulines model, which provided poorer results in two cases. The inversion method provided relatively good estimates for the water retention curves and also for the saturated conductivity when the moisture range explored was not too small. Water content time series were very well reproduced for the artificial soil and a sandy loam soil, but for two silt loam soils, larger errors were observed. The prediction of the water transfer behavior in the soil columns was poor when flow properties were estimated using directly determined hydraulic properties. The main limiting factor for applying the inversion method, particularly for nonsandy soils, was the characterization of the initial conditions in terms of the pressure head profile. Furthermore, the use of only soil moisture data is essential to enable the hydrogeophysical characterization of soils.

Data requirements for identifying macroscopic water stress parameters: A study on grapevines

We have tested the inverse modeling approach to derive macroscopic water stress parameters (MWSP) using different types of information, such as soil water content, pressure head, and transpiration rate. This testing was performed by numerical experiments considering a multilayered soil growing grapevines under three different irrigation regimes and two contrasted water stress scenarios. The results indicate that measurements of the soil water content alone do not contain enough information to estimate MWSP. Nonuniqueness is likely to occur, and the MWSP estimates may contain large uncertainties. However, the incorporation of only transpiration measurements into the objective function does allow the deriving of accurate MWSP. This contrast is mainly due to the difference of sensitivity to the MWSP, which is much higher for the transpiration than for the soil water content. Results obtained using only soil water pressure head measurements are similar to or poorer than those obtained using transpiration data. Moreover, visual inspection of response surfaces of the objective function suggests that the incorporation of further information in addition to transpiration into the objective function is not of great value for the identification of MWSP. Uncertainties for the MWSP estimated using the three types of information combined are in most cases only 1.3 times smaller than when transpiration measurements alone are incorporated into the objective function. Beyond the specific results obtained for the estimation of MWSP we find that the parameters estimates and their associated uncertainties are strongly dependent upon the type, quantity, and quality of the information included into the objective function. Hence inverse modeling may provide a means to design better experiments.