The direct optimization of soil hydraulic parameters (SHP) in unconstrained parameter space introduces significant uncertainties in ecohydrological modeling, particularly when addressing the complex model structure of Richards’ equation combined with Penman-Monteith equation. Pedotransfer functions (e.g., the latest version of Rosetta 3), which have been extensively trained using abundant soil hydraulic data, could potentially provide a physical constraint for sampling SHP. This study integrates optimization algorithms (Particle Swarm Optimization, PSO; Markov Chain Monte Carlo, MCMC; Sequential Monte Carlo, SMC; Generalized Likelihood Uncertainty Estimation, GLUE) with two sampling strategies − direct optimization of SHP and indirect optimization of SHP derived from soil particle composition (SPC) using Rosetta 3 − to evaluate their performance in ecohydrological modeling under predefined soil conditions. The results demonstrated that indirect optimization of SHP significantly enhances the accuracy in recovering predefined true parameters and states, and reduces the uncertainty of ecohydrological modeling compared to direct optimization of SHP. Specifically, the mean quartile deviation of biases in soil water content and evaporation were reduced from 0.0347 m³/m³ and 0.0027 m/h to 0.0061 m³/m³ and 0.0010 m/h, respectively. Furthermore, integration of the Rosetta 3 diminished the dimensionality of inverse modeling, thereby significantly enhancing algorithm convergence speed and precision. It is recommended to integrate Rosetta 3 with various optimization algorithms to enhance the accuracy of ecohydrological modeling.

Amplified eruptive outbreaks of bark beetles as a consequence of climate change can cause tree mortality that significantly affects terrestrial water and carbon fluxes. However, the lack of field-scale observations of underlying physiological mechanisms currently hampers the expression of such ecosystem disturbances in predictive modelling. Based on a unique flux tower dataset from a subalpine forest located in the Rocky Mountains, mechanisms of stomatal response to an extensive bark beetle outbreak were investigated using various models and parametrizations. The datasets cover a decade, including the periods of pre-infestation, infestation, and post-infestation. Field measurements showed considerable decreases in evapotranspiration (ET), transpiration (T), and leaf area index (LAI) during the two-year infestation period compared to the pre-infestation period. Model interpretations of observed water and carbon fluxes indicated that the overall reductions in T were not solely due to decreased LAI, but also to changes in physiological behaviours. The summer season's canopy-scale stomatal conductance was significantly reduced during the infestation period, from 0.0018 to 0.0011 m s⁻¹. One primary reason for the observed variations is likely that the bark beetle infestation hampers the water transport in the xylem. The damage of xylem has important implications for water use efficiency (WUE), which also significantly influences the parameterization of stomatal conductance. When using stomatal conductance models to forecast ecosystem dynamics, it is crucial to recalibrate the model's parameters to ensure the accurate depiction of stomatal dynamics during various infestation periods. The neglect of the temporal variability of canopy-scale stomatal conductance under ecosystem disturbances (e.g., bark beetle infestations) in current earth system models, therefore, requires specific attention in assessments of large-scale water and carbon balances.

Soil hydraulic parameters are influenced by various inherent soil properties, such as pore structure and organic matter content, which can vary with changes in the ecosystem. However, identifying the temporal variations of soil hydraulic parameters in a co-evolving soil-vegetation system remains a challenge. This study focused on a tropical forest with significant seasonal variations in vegetation attributes, evaporation, and carbon fluxes over a five-year monitoring period. The particle batch smoother algorithm was integrated with an unsaturated flow model to identify the seasonally varied soil hydraulic parameters through assimilation of in-situ measured soil moisture. As a benchmark, the Generalized Likelihood Uncertainty Estimation method was applied to optimize soil hydraulic parameters without considering temporal variation. The results indicated that the temporally varying soil hydraulic parameters exhibited regular seasonal patterns and outperformed the unvaried soil hydraulic parameters in terms of reducing the errors in modeling of soil moisture and evaporation. Moreover, the seasonal variations in soil hydraulic parameters were closely linked to changes in the litterfall and terrestrial carbon fluxes over time. Specifically, due to the hysteresis of the transformation from litterfall to soil organic matter, the accumulated litterfall in Hot-dry season can replenish the soil organic matter, resulting in an increase in field capacity and saturated hydraulic conductivity in the Hot-rainy season. However, the intense decomposition of soil organic matter under high temperature in Hot-dry season led to a decrease in field capacity and saturated hydraulic conductivity. This study emphasizes the value of the particle batch smoother algorithm in detecting temporal variations in soil hydraulic parameters within a coevolving soil-vegetation system, thereby contributing to a more comprehensive understanding of the intricate dynamics within the ecohydrological system under a changing environment.

The Jarvis-type model, which incorporates stress functions, is commonly used to describe the physiological behaviour of stomatal response in various vegetation species. However, the model has been criticized for its empirically formulated multiplicative equation, which may not accurately capture the mutual impact of intercorrelated stress factors, for example, vapour pressure deficit (VPD) and air temperature (T_a). This study proposed a modified Jarvis model that introduces reduction factors in the stress functions of VPD and T_a to provide the description of canopy conductance. We used sap flow data from a beech forest in the mid-latitude region of Centre Europe to inversely estimate the canopy conductance with optimized stress functions. Our findings reveal that two recommended parameterization strategies for general deciduous broadleaf forest (DBF) significantly overestimated the transpiration rate, with a maximum value of similar to 2 mm/day on rainless days. This suggested that the beech forest exhibited a distinct stomatal response compared to the general DBF category. By applying boundary line analysis to fit the parameters, both the unmodified and modified Jarvis models provided better simulations of transpiration, with relatively high Nash-Sutcliffe Efficiency (NSE) values of 0.75 and 0.77, respectively. These results indicated that modelling transpiration can be improved by refining the parameterization of canopy conductance, particularly for vegetation species with unique stomatal behaviours that deviated from the characteristics of their general vegetation type. The modified Jarvis model offers a more accurate description of canopy conductance and enhances the modelling of transpiration in vegetated areas, especially under dry environment conditions with relatively high VPD.

Numerical simulation of unsaturated soil hydrology relies on calibrated soil hydraulic parameters, which are subject to uncertainty due to imperfect information during the inverse modelling. This study investigates the effectiveness of reducing parameter uncertainty using the recently developed Rosetta 3 pedotransfer function. The GLUE method was employed for numerical modeling using the Darcy-Richards equation under two strategies for sampling Mualem-van Genuchten (MvG) parameters: the first uses conventional random generation of MvG parameters (GLUE-random), while the second adopts Rosetta 3 to transfer soil particle composition to MvG parameter (GLUE-Rosetta). Both approaches were used for inverse modeling of 9 typical soils, each with a recommended parameter set defined as true values and associated soil moisture dynamics as observations. The posterior parameters selected with both GLUE-random and GLUE-Rosetta show an equifinality phenomenon. GLUE-random fails to provide well-constrained posterior parameters to recover the pre-defined true values, and its posterior results of soil water characteristic curve (SWCC) and soil hydraulic conductivity function (HCF) are poorly constrained. In contrast, GLUE-Rosetta significantly improves the accuracy of the inversely-estimated soil hydraulic parameters, and the ensemble of posterior SWCC and HCF also encompasses the predefined true curves. The results demonstrate the effectiveness of using Rosetta 3 to reduce the dimensionality of the optimization problem, which results in reliable estimation of soil hydraulic parameters and soil particle compositions. Moreover, GLUE-Rosetta outperforms GLUE-random in predicting soil moisture dynamics under different rainfall intensities. Overall, it is recommended to integrate Rosetta 3 with existing optimization tools to reduce the uncertainty of soil parameters and support more reliable modeling of unsaturated soil hydrology.

Stability analysis of unsaturated landslide deposits requires reliable estimates of soil moisture and pore water pressure. However, modeled soil moisture and pore water pressure contain substantial uncertainties due to imperfect information on soil hydraulic properties. Due to the relatively high dimensionality, commonly used parameter optimization strategies can be significantly affected by equifinality problems. This study investigates the effectiveness of reducing parameter estimation dimensionality using soil pedo-transfer functions. Specifically, we first estimated soil hydraulic parameters using the traditional Generalized Likelihood Uncertainty Estimation (GLUE) method, with parameters randomly drawn from the entire space (refer to as GLUE-random). In a second strategy, we use the Rosetta 3 pedotransfer function to constrain soil hydraulic parameters (refer to as GLUE-Rosetta). The two methods were tested in a typical landslide deposit with in-situ measured soil moisture dynamics for inverse modeling. The GLUE-random estimated soil hydraulic parameters contained substantial uncertainties –resulting in poorly constrained soil water retention curves (SWCC) and hydraulic conductivity functions (HCF). As a result, the uncertainty bands of pore water pressure and slope stability can cross values with several orders of magnitudes. In contrast, GLUE-Rosetta provided well-constrained SWCC and HCF, which significantly reduce the uncertainties in pore water pressure and slope stability estimates. These results suggest that the Rosetta 3 pedotransfer function can significantly improve the reliability of soil hydraulic parameters by reducing the dimensionality of the optimization problem and high-quality prior information of soil hydraulic properties. In conclusion, Rosetta 3 can enhance the reliability of soil parameters estimates and the reliability of subsurface hydrology, which may benefit the development of landslide early-warning systems.

Extreme rainfall is a common triggering factor of landslide disasters, for infiltration and pore water pressure propagation can reduce suction stress and shear strength at the slip surface. The subsurface hydrological model is an essential component in the early-warning system of rainfall-triggered landslides, whereas soil moisture and pore water pressure simulated by the Darcy–Richards equation could be significantly affected by uncertainties in soil hydraulic parameters. This study conducted an inverse analysis of in situ measured soil moisture in an earthquake-induced landslide deposit, and the soil hydraulic parameters were optimized with the Differential Evolution Markov chain Monte Carlo method (DE-MC). The DE-MC approach was initially validated with a synthetic numerical experiment to demonstrate its effectiveness in finding the true soil hydraulic parameters. Besides, the soil water characteristic curve (SWCC) and hydraulic conductivity function (HCF) described with optimized soil hydraulic parameter sets had similar shapes despite the fact that soil hydraulic parameters may be different. Such equifinality phenomenon in inversely estimated soil hydraulic parameters, however, did not affect the performance of simulated soil moisture dynamics in the synthetic numerical experiment. The application of DE-MC to a real case study of a landslide deposit also indicated satisfying model performance in terms of accurate match between the in situ measured soil moisture content and ensemble of simulations. In conclusion, based on the satisfying performance of simulated soil moisture and the posterior probability density function (PDF) of parameter sets, the DE-MC approach can significantly reduce uncertainties in specified prior soil hydraulic parameters. This study suggested the integration of the DE-MC approach with the Darcy–Richards equation for an accurate quantification of unsaturated soil hydrology, which can be an essential modeling strategy to support the early-warning of rainfall-triggered landslides.