| 23 Apr 2026
Quantifying key parameter sensitivities for water table depth in hydrological schemes of CoLM-PSUADE
Abstract. Accurately representing groundwater dynamics in land surface models (LSMs) is crucial for understanding water-energy cycles and assessing water resources. However, most LSMs lack systematic sensitivity analyses of parameters regulating water table depth (WTD). This study couples the Common Land Model (CoLM) with Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE) in a single-point framework to facilitate systematic parameter analysis and calibration aimed at improving WTD simulation. The CoLM-PSUADE framework was then applied to evaluate groundwater-related parameters using WTD observations from the Gongga Mountain site. A comprehensive analysis integrating qualitative sensitivity analysis, quantitative sensitivity analysis, and parameter optimization techniques was conducted to evaluate the sensitivity of 56 parameters associated with key hydrological processes and to determine their optimal ranges. The results indicate that eight parameters can be identified as robustly sensitive, including those controlling unsaturated soil water movement (56-soil_alpha, 53-soil_n), subsurface runoff (40-rsubmax), plant hydraulic processes (49-beta, 45-krmax, 46-ck0), and net surface water infiltration (4-alpha_rain, 10-rhol_nir). Among them, the subsurface runoff parameter rsub,max exhibits a well-defined optimal range (on the order of 10⁻⁴) and can regulate both the magnitude of subsurface runoff and its decay with increasing WTD when combined with another empirical parameter in the SIMTOP (Simple TOPMODEL-based) scheme, fdrai, thereby exerting strong control on WTD. The soil hydraulic parameter α shows the highest sensitivity. It regulates unsaturated hydraulic conductivity and soil water retention, thereby exerting a dominant influence on the variability and lagged response of WTD. Based on these findings, a stepwise calibration strategy is recommended, in which the subsurface runoff parameters (rsub,max and fdrai) are first adjusted to constrain the mean WTD, followed by optimization of other key parameters, such as α, to improve the temporal dynamics of WTD. It is demonstrated that CoLM-PSUADE provides a useful tool for sensitivity-guided parameter optimization in high-dimensional LSMs and hydrological models.
The authors have developed and applied a CoLM2024–PSUADE coupling framework to examine 56 hydrologically relevant parameters using multiple sensitivity-analysis methods. They further integrate parameter screening, quantitative sensitivity analysis, and parameter optimization to evaluate and improve the simulation of water table depth at a mountain forest site. Overall, I appreciate the considerable effort invested in model coupling, experimental design, and the subsequent analyses. The study addresses an important topic and presents a potentially useful workflow for sensitivity-guided calibration in land surface models. However, several aspects of the experimental design and the interpretation of the results would benefit from further clarification and strengthening before publication.
Major comments:
The role and notation of the subsurface runoff decay factor require further clarification. In Table 2, the 56 adjustable parameters include 38-fsatdcfa, which is described as a runoff decay parameter, but the later discussion refers to the decay factor as fdrai. It is therefore unclear whether fdrai is identical to 38-fsatdcfa or represents a separate parameter. I suggest that the authors clarify the relationship between 38-fsatdcfa and fdrai.
In addition, Figure 16 suggests that although the SCE-optimized simulation improves the overall WTD level, it does not fully reproduce the two pronounced WTD declines observed in 2007. This point deserves further discussion, because capturing such event-scale or seasonal drawdown dynamics is important for evaluating whether the optimized parameters improve not only the mean WTD but also the temporal variability. The authors should clarify whether these discrepancies are related to limitations in the runoff/groundwater parameterization, freeze-thaw processes, meteorological forcing, observational uncertainty, or the optimization objective function. It would also be helpful to add event-based or seasonal performance metrics to quantify model performance during these pronounced drawdown periods.
Minor comments: