Understanding the effects of climate change on plant phenological dynamics and growth patterns is critical for predicting climatic changes on the Qinghai-Tibetan Plateau (QTP). We used data over 21 years (1997 to 2017) for four dominant species on the QTP, namely Astragalus laxmannii (legume), Artemisia scoparia (forb), Kobresia humilis (sedge), Stipa purpurea (grass), and examined the relationships among climatic changes, plant phenology, growth pattern, and biomass. Most phenological periods in Stipa purpurea and Artemisia scoparia were delayed, whereas in Astragalus laxmannii, they were advanced. Soil temperature and maximum air temperature were the most important drivers. There were trade-offs between reproductive phenology and vegetative phenology, as well as between the length of the rapid growth period and the intrinsic growth rate. The impacts of the phenological or growth processes were species-specific. Our findings provide evidence of long-term changes and are of great significance for improving the accuracy of models.

The Soil and Water Assessment Tool (SWAT) model has been widely applied for simulating the water cycle and quantifying the influence of climate change and anthropogenic activities on hydrological processes. A major uncertainty of SWAT stems from poor representation of vegetation dynamics due to the use of a simplistic vegetation growth and development module. Using long-term remote sensing-based phenological data, we improved the SWAT model’s vegetation module by adding a dynamic growth start date and the dynamic heat requirement for vegetation growth rather than using constant values. We verified the new SWAT model in the Han River basin, China, and found its performance was much improved in comparison with that of the original SWAT model. Specifically, the accuracy of the leaf area index (LAI) simulation improved notably (coefficient of determination (R2) increased by 0.193, Nash–Sutcliffe Efficiency (NSE) increased by 0.846, and percent bias decreased by 42.18%), and that of runoff simulation improved modestly (R2 increased by 0.05 and NSE was similar). Additionally, we found that the original SWAT model substantially underestimated evapotranspiration (Penman–Monteith method) in comparison with the new SWAT model (65.09 mm (or 22.17%) for forests, 92.27 mm (or 32%) for orchards, and 96.16 mm (or 36.4 %) for farmland), primarily due to the inaccurate representation of LAI dynamics. Our results suggest that accurate representation of phenological dates in the vegetation growth module is important for improving the SWAT model performance in terms of estimating terrestrial water and energy balance.