| 23 Feb 2026
Evidence of Vertical Soil Hydraulic Heterogeneity Regulating Hydrothermal Simulations in Qinghai–Tibetan Plateau Wetlands
Abstract. Alpine wetlands on the Qinghai-Tibetan Plateau host vertically structured and highly contrasting pore systems that fundamentally shape land and atmosphere exchanges, yet their hydraulic expressions and process implications remain poorly quantified. This study provides the first process based and depth resolved characterization of these layered pore structures using soil physical analyses and laboratory evaporation experiments. The derived Clapp–Hornberger parameters reveal coherent hydraulic contrasts, with surface layers dominated by macropore connectivity and showing high θs and Ks and low b that promote rapid drainage and evaporation, mid layer domains with lower θs and Ks and larger b that enhance retention in finer pores, and deeper layers that act as stable and persistent storage reservoirs. These properties together generate a vertical regime of rapid near surface drainage, delayed mid layer release, and long lasting deep moisture storage. When implemented in Noah-MP, this hydraulic stratification systematically altered water and energy partitioning during wet and dry periods and showed that vertical hydraulic heterogeneity rather than a single layer parameterization governs the timing and magnitude of evaporation and heat fluxes. These findings provide the first quantitative evidence that pore scale structure regulates profile scale hydrothermal responses in Qinghai-Tibetan Plateau wetlands and establish a physically grounded basis for representing vertically heterogeneous hydraulic processes in land surface models.
This manuscript investigates the vertical heterogeneity of soil hydraulic properties in a typical alpine wetland on the Qinghai–Tibet Plateau and its impact on land-surface hydrothermal simulations. The authors combine CT imaging, HYPROP-WP4C evaporation experiments, CH curve fitting, and Noah-MP modeling to conduct a comprehensive analysis. This integrated approach is commendable, and the findings are meaningful for land-surface simulations, especially for alpine wetlands on the Qinghai–Tibet Plateau. However, several concerns need to be addressed before the manuscript can be recommended for publication.
Recommendation: Moderate Revision
Comments:
1. The authors focus on a single site in the Sanjiangyuan region, and the model evaluation is based primarily on data from 2023. While these data are adequate for offline Noah-MP simulations, the manuscript claims that the findings provide a verifiable pathway for alpine wetlands on the Qinghai–Tibet Plateau and similar complex ecosystems. It is therefore important to clarify how representative this site is of wetlands across the Qinghai–Tibet Plateau. In addition, was 2023 a climatologically normal year for this region? The authors are encouraged to expand Section 2.1 to include this information.
2. In Section 2.5, the authors combined data from two instruments (HYPROP-2 and WP4C) to obtain continuous water-retention curves (WRCs) and hydraulic-conductivity curves (HCCs). However, it is unclear how discrepancies between the two datasets were handled in the transition zone—specifically, whether the overlapping region was directly spliced or whether smoothing and fitting procedures were applied.
3. The authors employed the SCE-UA algorithm to invert multiple CH parameters. However, it remains unclear whether multiple independent optimization runs were conducted and whether parameter non-uniqueness was observed.
4. In the model experiment design (Section 2.8), the OAT sensitivity approach was adopted. Although the authors included a control experiment using Noah-MP default values, this appears insufficient. An OAT design cannot capture the nonlinear interactions among the selected parameters. The authors are therefore encouraged to add further experiments to examine such nonlinear interactions.
5. In Section 3.3, Pearson correlation coefficients are reported; however, the manuscript does not clearly state the sample size, p-values, or confidence intervals associated with these analyses, making it difficult to assess the robustness of the reported relationships.