Soil parameterization in land surface models drives large discrepancies in soil moisture predictions across hydrologically complex regions of the contiguous United States

Land surface models (LSMs) are critical components of Earth system models (ESMs), enabling the simulation of energy and water fluxes that are essential for understanding climate systems. Soil hydraulic parameters, derived using pedotransfer functions (PTFs), are crucial for modeling soil–plant–water interactions; they introduce uncertainties in soil moisture simulations. However, a key knowledge gap exists in understanding how specific soil hydraulic properties contribute to these uncertainties and in identifying the regions most affected by them. This study conducts an intra-model sensitivity analysis within the Community Land Model version 5 (CLM5), examining how alternative soil parameter settings influence soil moisture variability across the contiguous United States (CONUS) using empirical orthogonal function (EOF) analysis. The EOF analysis revealed dominant spatial and temporal patterns of soil moisture across multiple experimental configurations, highlighting the impact of soil parameter variability on hydrological processes. The results showed significant discrepancies in soil moisture simulations, particularly in the central Great Plains, which may be attributed to the combination of arid climatic conditions and limitations in modeling saturated hydraulic conductivity and soil water retention curves. Seasonal soil moisture dynamics showed broad similarity to ERA5-Land patterns, with differences in magnitude and phase, indicating the importance of refined parameterization, particularly in the representation of infiltration and drainage processes. Comparisons with ERA5-Land, used here solely as a model-based reference for pattern consistency, revealed stronger similarity in regions with consistent climatic gradients but persistent differences in hydrologically complex areas, particularly in areas with arid climates, such as the Great Plains, where hydrological processes remain difficult to represent. Because CLM5 is forced by GSWP3, whereas ERA5-Land is an offline HTESSEL replay forced by ERA5, differences reflect both forcing and structural contrasts in addition to parameter effects. This research demonstrates the necessity to refine soil parameter representations, utilize high-resolution datasets, and consider climatic variability to inform the model development of LSMs. Importantly, these findings also pave the way for future efforts that incorporate dynamic soil properties into LSMs. This work illustrates the influence of soil properties on simulated variability. While the analysis documents their importance, a future direction will be to develop approaches that allow these properties to vary dynamically within LSMs. This study contributes to ongoing efforts toward more integrated modeling frameworks that capture soil–hydrology–climate interactions.