Cloud-Model-Based Evaluation of Reference Evapotranspiration Variability for Reference Crops Within the Xizang Plateau’s Agricultural Regions

Against the backdrop of ongoing climate change, the Qinghai–Tibet Plateau, a region highly sensitive to climatic variation, exhibits intricate spatiotemporal patterns in reference crop evapotranspiration (ETO), with significant implications for regional water-resource planning. This study selected four agro-climatic zones across the plateau region (TSA, TSH, TAZ, and WCH). Long-term daily observations from 28 meteorological stations were used to estimate ETO via the FAO 56 Penman–Monteith equation. This extensive dataset enabled robust trend analysis using the Mann–Kendall test, alongside a cloud-model framework, and analyses of sensitivity and contributions to evaluate ETO’s spatiotemporal evolution, its distributional uncertainty, and the underlying drivers. Results reveal pronounced regional heterogeneity in the interannual variability of ETO. Annual ETO declined in TSH and TSA (trend rates of −1.12 and −6.58 mm·10a−1, respectively) and increased in TAZ and WCH (15.76 and 10.74 mm·10a−1, respectively). At monthly and seasonal timescales, ETO exhibited an unimodal pattern, with the greatest stability in winter and spring and lower stability in summer and autumn. The cloud-model parameter He indicates that ETO stability is greatest in TSH and weakest in WCH, with He values of 7.15 and 12.29 mm, respectively. Contribution-rate analyses identify Tmax and Tmean as the principal determinants of rising ETO across all study zones, reflecting the largest individual contributions. Temperature-related factors together account for the majority of ETO variability across the regions, with their absolute contributions ranging from 5.61% to 8.63%, well above those of aerodynamic factors (0.62–1.78%). Stability assessments indicate that ETO is generally more unstable than its meteorological drivers, with substantial regional disparities, implying that ETO evolution cannot be explained by a single controlling factor. Overall, the study characterizes the uncertainty in ETO variations under complex terrain, highlights the value of the cloud model for refined hydrological assessments, and provides a scientific basis for adaptive agricultural water-resource management in the region.