Abstract. Soil micronutrient supply sustains critical ecological functions but exhibit poorly quantified distribution patterns in high-altitude ecosystems. This study bridges this knowledge gap through a large-scale investigation across the Tibetan Plateau, a cold-arid region where cryogenic weathering, aridity, and suppressed pedogenesis interact to govern micronutrient cycling. We assembled a plateau-wide dataset from 526 sites with triplicate surface soils (0–10 cm) per site (n = 1660). Four micronutrients (Fe, Mn, Zn, and V) were measured by laboratory X-ray fluorescence (XRF) for all samples and calibrated and validated against inductively coupled plasma mass spectrometry (ICP-MS) for a subset of samples.Four micronutrients were paired with multi-source predictors (climate, vegetation, soil covariates, topography, grazing disturbance, and weathering proxy). Elemental contents span broad ranges, with site-level summaries (mean ± SD, mg kg−1) of Fe 22 864.30 ± 7589.01, Mn 576.74 ± 206.44, Zn 27.24 ± 8.55, and V 56.99 ± 19.33. Random Forest model was employed to quantify controls and generate high-resolution spatial maps. Key results reveal that pronounced regional heterogeneity is driven primarily by weathering intensity with secondary modulation from climate and topography covariates. Element-specific spatial patterns were observed, with Fe enrichment in southeastern/southern plateau, Mn gradients increasing southeastward. Zn hotspots in central-eastern and western marginal zones, and V exhibits a moderate spatial gradient, with higher contents in the southeastern Tibetan Plateau and relatively lower values in the northwest. We provide 1 km maps of all four micronutrients together with pixel-wise uncertainty layers to support benchmarking of process-based micronutrient cycling models and to inform sustainable ecosystem management under climate change. Predictions for major elements are robust, whereas trace-level elements, particularly Zn, exhibit comparatively higher methodological uncertainty despite calibration. Accordingly, users should interpret absolute Zn contents with caution and refer to the accompanying uncertainty diagnostics when applications require high-precision estimates. The dataset is openly available at TPDC (https://doi.org/10.11888/Terre.tpdc.303242, Huo et al., 2026).
Huo et al. (Fri,) studied this question.