关键词: 淮河流域, 平均气温, 变化趋势, 数据非均一性, 贡献率

Abstract: Long homogeneous meteorological data is important to study climate change. Evaluating the impact of data inhomogeneity on analyzing average temperature trends in Huai river basin is value for accurately understanding the response of agriculture, ecology and water resources to climate change. In this study, based on the homogeneous data and observation data of daily average air temperature from the National Meteorological Information Center, the trends of annual and seasonal air temperatures during 1961 to 2018 were calculated using simple linear regression at 172 meteorological stations over Huai river basin. Then the impacts of data inhomogeneity on analyzing mean air temperature trends during 1961 to 2018 were evaluated by using two terms which were inhomogeneity impacts and contribution rates. The results showed that the average air temperature series of 96 meteorological stations in Huai river basin were inhomogeneous, accounting for 55.8% of the 172 total stations. Before and after the homogenization, the regional annual average air temperature both increased significantly. But the increasing rate was underestimated due to the data heterogeneity, and the influence was −0.015℃·10y⁻¹ with a contribution of −6.6%. For each station, 35 stations (20.3%) was positively affected and the warming rate was overestimated with an average contribution of 21.3%, while 61 stations (35.5%) were negatively affected and the warming rate was underestimated with an average contribution of −43.6%. The influences of inhomogeneity for four seasons showed little difference, which were −0.016℃·10y⁻¹，−0.014℃·10y⁻¹，−0.016℃·10y⁻¹ and −0.015℃·10y⁻¹, respectively. However, because of the slowest increasing rate, the absolute value of inhomogeneity contribution was largest in summer with a contribution of −40.0%. The contributions of spring, autumn and winter were −4.7%、−8.0% and −4.3%. Inhomogeneity mostly affected the temperature rising rate at each station in spring, autumn and winter, while led to a turning of temperature trend before and after homogenization at 20 stations (11.6%) in summer.

Key words: Huai river basin, Mean air temperature, Variation trend, Data Inhomogeneity, Contribution rate