CLIGEN非降水要素在黄土塬区的适用性评估

Assessing the Applicability of CLIGEN Non-precipitation Parameters on Loess Tableland

许多基于物理机制的水文和作物模型需要日序列气象数据来驱动，CLIGEN是为WEPP等模型产生气候输入文件的天气发生器，可以产生10个日序列气象变量来满足这种需要，但是其在中国的适用性需要进行评估。研究的目标是利用黄土高原陕西长武1957～2001年的气象数据评估CLIGEN产生非降水要素（最高温度、最低温度、露点温度、太阳辐射和风速）的能力。结果表明，CLIGEN对最高温度、最低温度和露点温度的模拟效果较好，对太阳辐射和极端气候事件的模拟效果较差，对风速的模拟效果最差。相关性检验表明CLIGEN很好地保持了气象要素的季节性，这对模拟农业生产是非常重要的；但是没有保留气象要素逐日的自相关和互相关性，进而导致产生的温度变化不符合连续渐变的规律。

Daily weather data are required by many physically based hydrological and crop models to study the potential impacts of climate change, which are often generated by stochastic daily weather generators. CLIGEN is such kind of weather generator that can generate ten daily weather variables to meet this need, and is used to provide climate input data to the WEPP （Water Erosion Prediction Project） model for predicting runoff, soil erosion and crop yield, however, its applicability in China needs to be assessed. T test and F test were used to evaluate the ability of CLIGEN to reproduce the average and variance of the measured data. Skewness, kurtosis, cumulative distribution and rank sum test were employed to check the distribution of the generated weather data. The raw and standard data were used to test the day to day and seasonal correlation. The objectives of this study were to evaluate the ability of CLIGEN to generate non-precipita- tion parameters including daily maximum, minimum and dew point temperatures, solar radiation and wind velocity based on the measured daily weather data during 1957 - 2001 of Changwu County on the Loess Plateau. Result showed that CLIGEN reproduced daily maximum, minimum and dewpoint temperature well due to the little differences of average and variance between the measured and the generated data; however, the relative error of generated solar radiation and wind was 3.9% and 118% respectively, and the generated variance was far different from the measured. The skewness and kurtosis of the generated data was no more than the generated, which implied that CLIGEN can not generate climate extreme events well. Rank sum test and cumulative distribution analysis showed that CLIGEN didn＇ t preserve the distribution of the measured data well. The correlation analysis showed that CLIGEN reproduced the seasonality of climate variation well, which is important for agricultural simulation, but it didn＇ t preserve the proper day-to-day serial and cross correlation between daily temperatures and solar radiation, which led to the simulated temperature not change in a gradual and continuous manner. Though there are some weaknesses, CLIGEN would still have great application prospect after calibration with proportional methods according to the site-specific conditions.