###### Abstract  
Simultaneous simulations of liquid water, water vapor, and heat transport are essential for modeling unsaturated hydrological processes, especially in semi-arid and arid regions. Modeling such coupled hydrothermal processes greatly depends on accurate estimations of soil hydraulic and thermal properties. However, many contributions for estimating these parameters using inversion methods use a single observation as the objective variable, e.g., soil water content is the most common. This study employ multi-objective algorithms to evaluate the worth of different observation types in simultaneous estimations of the soil hydraulic and thermal properties in Inner Mongolia, China. The coupled hydrothermal processes are quantified by HYDRUS-1D model, within which a multialgorithm, genetically adaptive multiobjective (AMALGAM) algorithm is employed to investigate four types of observations that may be available including soil water content, soil temperature, matrix potential, and heat flux in soil profiles. Different combinations of the four measurement types are considered as objectives, resulting single-, dual-, triple-, and quadruple-objective optimization schemes. The results demonstrate that incorporating additional observation types, such as soil water content and matrix potential, significantly improves the overall simulation accuracy of the coupled model. Particularly, the soil water movement is closely linked to the observation of water content, which plays a crucial role in the inversion process. While adding temperature or heat flux to the multi-objective optimization further refines the accuracy of inversion. Considering the cost-benefit ratio of different observation types, simultaneous measurement of water content and temperature is the most practical approach for the inversion since these two variables can be observed simultaneously by the same set of probes such as with a TDR. coupled water vapor heat transport; soil hydraulic properties; soil thermal properties; HYDRUS-1D; a multialgorithm genetically adaptive multiobjective (AMALGAM); multivariate multiobjective optimization +
Footnote †: journal: Applied sciences  
an important role. Although it is widely believed that the movement of water and heat in the soil is coupled, the complexity and significant spatial and temporal variability of the coupled water-vapor- heat movement mechanism in soils make the coupled modeling challenging and limited. Modeling of the coupled water, vapor, and heat transports heavily relies on knowledge of the soil hydraulic and thermal properties at the scale of interest. Precise estimation of these properties is crucial for reliable model simulations and predictions [2; 3; 4; 5]. The complexity of the soil-water system makes it difficult and labor-intensive to directly measure the hydraulic properties, in particular in the presence of spatial variability and scaling issue. Earlier studies have focused on laboratory or field experiments with well-defined boundary conditions to determine hydraulic properties of unsaturated soils [6; 7; 8]. Although these experiment methods have the advantage of being relatively cost effective and simple to implement, they are often relatively time-consuming, restricted to the imposed boundary condition, and not representative for the hydrological processes at larger spatial scales [9]. Therefore, during the last two decades, significant efforts have been made on development and application of inverse modeling to estimate the unsaturated soil properties at different spatial scales in various ways from Monte-Carlo based stochastic methods [10; 11; 12; 13]. The inversions is increasingly carried out as the increasing capabilities of computational power, robust and efficient parameter optimization approaches, and continued advances in measurement technologies [8; 14]. In inverse modeling of hydrological processes in vadose zone, the unknown hydraulic and thermal parameters are estimated by minimizing deviations between observed and model predicted variables.