Estimation of root water uptake parameters by inverse modeling with soil water content data

In this paper we have tested the feasibility of the inverse modeling approach to derive root water uptake parameters (RWUP) from soil water content data using numerical experiments for three differently textured soils and for an optimal drying period. The RWUP of interest are the rooting depth and the bottom root length density. In a first step, a thorough sensitivity analysis was performed. This showed that soil water content dynamics is relatively insensitive to RWUP and that the sensitivity depends on the texture of the considered soil. For medium-fine textured soil, the sensitivity is particularly low due to relatively high unsaturated hydraulic conductivity values. These ones allow a “compensating effect” to occur, i.e., vertical unsaturated water fluxes overshadowing in some way the root water uptake. In a second step, we analyzed the well-posedness of the solution (stability and nonuniqueness) when only RWUP are optimized. For this case, the inverse problem is clearly ill-posed except for the estimation of the rooting depth parameter for coarse and the very fine textured soils. In a third step, we addressed the case where RWUP are estimated simultaneously with additional parameters of the system (i.e., with soil hydraulic parameters). For this case, our study showed that the inverse problem is well-posed for the coarse and very fine textured soils, allowing for the estimation of both RWUP of interest provided that a powerful global optimization algorithm is used. On the contrary, the estimation of RWUP is unfeasible for medium-fine textured soil due to the “compensating effect” of the vertical unsaturated water flows. In conclusion, we can state that the inverse modeling approach can be applied to derive RWUP for some soils (coarse and very fine textured) and that the feasibility is strongly improved if the RWUP are simultaneously optimized with additional parameters. Nevertheless, more detailed research is needed to apply the inverse modeling approach to real cases for which additional issues are likely to be encountered such as soil heterogeneity and root dynamics

Estimating spatial mean root-zone soil moisture from point-scale observations

Root zone soil moisture is a key variable in many land surface hydrology models. Often, however, there is a mismatch in the spatial scales at which models simulate soil moisture and at which soil moisture is observed. This complicates model validation. The increased availability of detailed datasets on space-time variability of root-zone soil moisture allows for a posteriori analysis of the uncertainties in the relation between point-scale observations and the spatial mean. In this paper we analyze three comprehensive datasets from three different regions. We identify different strategies to select observation sites. For instance, sites can be located randomly or according to the rank stability concept. For each strategy, we present methods to quantify the uncertainty that is associated with this strategy. In general there is a large correspondence between the different datasets with respect to the relative uncertainties for the different strategies. For all datasets, the uncertainty can be strongly reduced if some information is available that relates soil moisture content at that site to the spatial mean. However this works best if the space-time dynamics of the soil moisture field are known. Selection of the site closest to the spatial mean on a single random date only leads to minor reduction of the uncertainty with respect to the spatial mean over seasonal timescales. Since soil moisture variability is the result of a complex interaction between soil, vegetation, and landscape characteristics, the soil moisture field will be correlated with some of these characteristics. Using available information, we show that the correlation with leaf area index or a wetness coefficient alone is insufficient to predict if a site is representative for the spatial mean soil moisture content

The influence of conceptual (mis)match on collaborative referring in dialogue

When two dialogue partners need to refer to something, they jointly negotiate which referring expression should be used. If needed, the chosen referring expression is then reused throughout the interaction, which potentially has a direct, positive impact on subsequent communication. The purpose of this study was to determine if the way in which the partners view, or conceptualise, the referent under discussion, affects referring expression negotiation and subsequent communication. A matching task was preceded by an individual task during which participants were required to describe their conceptualisations of abstract tangram pictures. The results revealed that participants found it more difficult to converge on single referring expression during the matching task when they initially held different conceptualisations of the pictures. This had a negative impact on the remainder of the task. These findings are discussed in light of the shared versus mutual knowledge distinction, highlighting how the former directly contributes to the formation of the latter

Impact of within-field variability in soil hydraulic properties on transpiration fluxes and crop yields: A numerical study

By means of numerical modeling we investigate the impact of within-field variability in the soil hydraulic properties on actual transpiration and dry matter yield for three different climate scenarios. We first show that the sensitivity of the simulated actual transpiration and dry matter yield to soil hydraulic parameters increases with the dryness of the climate. The numerical simulations with soil independent stress factors further demonstrate that the impact of the within-field variability in soil hydraulic properties on the simulated transpiration and dry matter yield can be very large. The generated spatial variability in transpiration and dry matter yield increases systematically with the dryness of the climate, with coefficients of variation increasing from 7 to 14.6% for actual transpiration and from 6.7 to 16% for dry matter yield. In a subsequent analysis, all agrohydrological simulations are rerun considering that the water stress parameters are spatially variable and soil dependent. While the results obtained with the adjusted water stress parameters are quite different, the spatial variability in simulated transpiration and dry matter yield still increases for drier conditions. The different results obtained, although not validated experimentally, illustrate that the use of an agrohydrological simulation model in a stochastic mode requires accurate estimates of the water stress parameters, which should be soil dependent. Finally, we show that simultaneous estimation of water stress and soil hydraulic parameters cannot be robustly performed using measurements of transpiration or dry matter yield alone. Consequently, the use of an agro-hydrological model in a stochastic mode for a vegetated surface requires alternative strategies for specifying reliable water stress parameters. Adjustment of water stress parameters from reference unsaturated hydraulic conductivity values seems attractive, but needs more research