5. Statistical Simulation of Plant Phenology Spatial Variation

Daily mean air temperature-based spatial phenology models were fitted and validated using Ulmus pumila first leaf unfolding and leaf fall end data at 46 stations within China’s temperate zone from 1986 to 2005. Results show that spatial patterns of first leaf unfolding and leaf fall end dates were obviously influenced by spatial patterns of daily mean air temperatures during the optimum spring and autumn length period, respectively. A higher location-specific multi-year mean spring and autumn temperature resulted in an earlier location-specific multi-year mean first leaf unfolding date and a later leaf fall end date. On average, a 1 °C spatial shift in multi-year mean spring and autumn temperatures may cause a spatial shift of −3.1 days and 2.6 days in multi-year mean first leaf unfolding and leaf fall end dates, respectively. Similar spatial relationships were also detected between daily mean temperature and phenological occurrence date in each year. The regression equations indicate that a 1 °C spatial shift in yearly mean spring and autumn temperatures may induce a spatial shift between −4.28 and −2.75 days in yearly first leaf unfolding date, and between 2.17 and 3.16 days in yearly leaf fall end date. Error estimation confirmed the reliability of multi-year mean and yearly spatial phenology models in simulating and predicting spatial patterns of Ulmus pumila first leaf unfolding and leaf fall end dates. Further analysis showed that a 1 °C spatial shift in mean spring temperature in warmer years may induce a larger spatial shift in first leaf unfolding date than that in colder years. Thus, future climate warming may enhance sensitivity of the spatial response of first leaf unfolding to temperature.