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Bulk density (BD) is one of the most important soil physical properties in the hydrological processes of Earth's Critical Zone (CZ). The heterogeneity and related controlling factors of surface BD have received extensive research attention. However, it is unclear whether the effects of controlling factors on BD are homogeneous or vary spatially. This study quantified topsoil BD with a high‐density sampling strategy (10 m × 10 m; n = 1134) across the ShuangChaGou watershed in the critical zone of the Chinese Loess Plateau (CLP‐CZ). Stepwise multiple linear regression (SMLR), regression kriging (RK), random forest (RF) and geographically weighted regression (GWR) were established as modelling approaches to (i) compare the performance of these four models in explaining BD variation, (ii) assess the main factors driving BD, and (iii) evaluate the spatial non‐stationary relationship between BD and its explanatory variables. GWR explained the BD variation to the same extent as the RK model (35%), outperforming both the SMLR (15%) and RF models (19%). The variation in topsoil BD depended mainly on: soil organic carbon (SOC) content, normalised difference vegetation index (NDVI), plan curvature, and slope length. The spatial distribution of the local R2, regression coefficient magnitude, significance and sign calculated by GWR revealed that the effects of variables accounting for variation in BD varied spatially. The explanatory power of these variables in terms of BD variation was strongest in northern and northeastern areas of the watershed and weakest in southern areas of the watershed. Both significant negative and positive correlations between BD and silt content, NDVI, and plan curvature were found. This study enriched traditional research linked to the complicated relationships between BD and controlling factors by incorporating spatial non‐stationarity, which is useful for improving the accuracy of hydrological modelling in the CLP‐CZ and in similar CZs worldwide. Highlights: Non‐stationary relationships between BD (N = 1134) and its explanatory variables were captured at the watershed scale.GWR model performed on par with the RK and better than the SMLR or RF model.Silt content, NDVI, and plan curvature showed both significant negative and positive effects on BD.Spatial non‐stationarity of BD should be considered for improving the performance of hydrological modeling. [ABSTRACT FROM AUTHOR]

• Surface K s variation (N = 1195) under different landscape units was explored across a watershed. • K s magnitude and variation differed significantly among Tableland, Gully-land and Slope-land. • BD, SSWC, silt, plan curvature, NDVI, and slope gradient shaped K s variations in the watershed. • Factors controlling K s variations were different under the three landscape units. • Distinct landscape units should be considered for evaluating the K s heterogeneity in a watershed. The spatial heterogeneity and related driving factors of the saturated hydraulic conductivity (K s) in the Earth's critical zones (CZs) have been documented widely in the literature. However, information regarding the effect of distinct landscape units (i.e., Slope-land, Gully-land, and Tableland) on such heterogeneity at the watershed scale in CZs characterized by complex topographic conditions remains scant. In the current study, we collected undisturbed and disturbed soil samples from the surface soil layer (0–5 cm) at 1195 sampling sites across the ShuangChaGou watershed (0.22 km2) in the Chinese Loess Plateau (CLP). According to classical and geostatistical analyses combined with the structural equation model (SEM), the mean K s followed the sequence Slope-land (0.25 cm·min−1) > Gully-land (0.187 cm·min−1) > All samples (0.186 cm·min−1) > Tableland (0.14 cm·min−1) (p < 0.05), with the K s values exhibiting moderate variations except for the Gully-land (strong variation). The LogK s spatial variation characteristics (i.e., the anisotropy ratio, effective range, and C/(C 0 + C)) considering the influence of topography varied among the landscape units and the entire watershed. The SEM identified that soil bulk density, saturated soil water content, silt content, plan curvature, NDVI, and slope gradient were the controlling factors in sequence affecting the K s variation for All samples (AS). In contrast, factors controlling K s variations were different under the Tableland, Gully-land, Slope-land, and AS. The magnitudes, spatial variations and controlling factors of the surface K s were greatly affected by the landscape units. Our study provided a robust assessment of the spatial variation of K s in CZs involving complicated topographic features, highlighting the utility of dividing distinct landscape units when evaluating the K s variation. A good understanding of this information is helpful for developing distributed hydrological models and for evaluating water and solute transportation in CZs of the CLP and similar regions around the world. [ABSTRACT FROM AUTHOR]