Considering film flow in the development of a soil hydraulic model has become a key method for improving model performance under dry conditions. With the considerable improvement in model performance, the trade‐off is often to increase the model complexity, thus limiting a model's application. In this paper, an alternative model is presented to predict soil hydraulic conductivities over the entire moisture range. In contrast to existing models that treat conductivity as a summation of the capillary and film components, the proposed model applies a single equation to capture the variation of the two conductivity components. The new model, therefore, presents a simple form, with no extra parameters added to the basic capillary models. Model testing with 24 soil data sets revealed that it was successful in predicting conductivities over the complete moisture range. A comparison with existing models demonstrated that the proposed model was generally in higher agreement with observations, achieving a much lower mean value of the root mean square errors for the predicted conductivities.

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Canopy conductance (g c ) is a critical component in hydrological modeling for transpiration estimate. It is often formulated as functions of environmental variables. These functions are climate and vegetation specific. Thus, it is important to determine the appropriate functions in g c models and corresponding parameter values for a specific environment. In this study, sap flow, stem water potential, and microclimatic variables were measured for three Drooping Sheoak (Allocasuarina verticillata) trees in year 2011, 2012, and 2014. Canopy conductance was calculated from the inversed Penman-Monteith (PM) equation, which was then used to examine 36 g c models that comprise different response functions. Parameters were optimized using the DiffeRential Evolution Adaptive Metropolis (DREAM) model based on a training data set in 2012. Use of proper predawn stem water potential function, vapor pressure deficit function, and temperature function improves model performance significantly, while no pronounced difference is observed between models that differ in solar radiation functions. The best model gives a correlation coefficient of 0.97, and root-mean-square error of 0.0006 m/s in comparison to the PM-calculated g c . The optimized temperature function shows different characteristics from its counterparts in other similar studies. This is likely due to strong interdependence between air temperature and vapor pressure deficit in the study area or Sheoak tree physiology. Supported by the measurements and optimization results, we suggest that the effects of air temperature and vapor pressure deficit on canopy conductance should be represented together.

A novel simple soil-plant-atmospheric continuum model that emphasizes the vegetation's role in controlling water transfer (v-SPAC) has been developed in this study. The v-SPAC model aims to incorporate both plant and soil hydrological measurements into plant water transfer modeling. The model is different from previous SPAC models in which v-SPAC uses (1) a dynamic plant resistance system in the form of a vulnerability curve that can be easily obtained from sap flow and stem xylem water potential time series and (2) a plant capacitance parameter to buffer the effects of transpiration on root water uptake. The unique representation of root resistance and capacitance allows the model to embrace SPAC hydraulic pathway from bulk soil, to soil-root interface, to root xylem, and finally to stem xylem where the xylem water potential is measured. The v-SPAC model was tested on a native tree species in Australia, Eucalyptus crenulata saplings, with controlled drought treatment. To further validate the robustness of the v-SPAC model, it was compared against a soil-focused SPAC model, LEACHM. The v-SPAC model simulation results closely matched the observed sap flow and stem water potential time series, as well as the soil moisture variation of the experiment. The v-SPAC model was found to be more accurate in predicting measured data than the LEACHM model, underscoring the importance of incorporating root resistance into SPAC models and the benefit of integrating plant measurements to constrain SPAC modeling.