CLIGEN生成干旱半干旱地区降水相关参数的验证

选用黄河上中游地区无定河流域为中心的15个气象站1959~1999年的降水日值资料,对随机天气发生器CLIGEN在干旱半干旱地区再现降水的能力进行了验证。结果表明:CLIGEN模型较好地模拟了该区域的降水发生概率;很好地再现了年、月、日降水总量平均值,平均相对偏差分别为2.4%、2.4%和2.1%;CLIGEN再现了96.4%的日降水变率、95.9%的月降水变率和84.1%的年降水变率。对年降水变率估计稍差,表明CLIGEN在模拟降水变率方面还有改进的必要。从降水极值看,年降水最大值的平均相对偏差为11.1%,偏差最大的是干旱区的临河站（39.1%）;年降水最小值的平均相对偏差为20.5%,偏差最大的是临河站（-30.7%）;月最大降水量除两站稍低外,其它站平均偏高20.2%;日降水最大值除临河站偏低3.4%外,其余各站平均偏高43.2%。总体上讲,CLIGEN在干旱地区的模拟能力比在半干旱区稍差。鉴于CLI-GEN模拟的极大值绝大部分都偏高,因此利用CLIGEN模型生成的降水资料运行径流和土壤侵蚀模型有高估径流量和土壤侵蚀量的可能,需要进一步利用自计雨量计的资料对CLIGEN生成次降水的参数进行验证,以确保径流和土壤侵蚀预测的精度。     

CLIGEN非降水要素在黄土塬区的适用性评估

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对最高温度和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。     

Evaluating and Extending CLIGEN Precipitation Generation for the Loess Plateau of China1

Abstract: Climate generator (CLIGEN) is widely used in the United States to generate long‐term climate scenarios for use with agricultural systems models. Its applicability needs to be evaluated for use in a new region or climate. The objectives were to: (1) evaluate the reproducibility of the latest version of CLIGEN v5.22564 in generating daily, monthly, and yearly precipitation depths at 12 stations, as well as storm patterns including storm duration (D), relative peak intensity (ip), and peak intensity (rp) at 10 stations dispersed across the Loess Plateau and (2) test whether an exponential distribution for generating D and a distribution‐free approach for inducing desired rank correlation between precipitation depth and D can improve storm pattern generations. Mean absolute relative errors (MAREs) for simulating daily, monthly, annual, and annual maximum daily precipitation depth across all 12 stations were 3.5, 1.7, 1.7, and 5.0% for the mean and 5.0, 4.5, 13.0, and 13.6% for the standard deviations (SD), respectively. The model reproduced the distributions of monthly and annual precipitation depths well (p > 0.3), but the distribution of daily precipitation depth was less well produced. The first‐order, two‐state Markov chain algorithm was adequate for generating precipitation occurrence for the Loess Plateau of China; however, it underpredicted the longest dry periods. The CLIGEN‐generated storm patterns poorly. It underpredicted mean and SD of D for storms ≥10 mm by ?60.4 and ?72.6%, respectively. Compared with D, ip, and rp were slightly better reproduced. The MAREs of mean and SD were 21.0 and 52.1% for ip, and 31.2 and 55.2% for rp, respectively. When an exponential distribution was used to generate D, MAREs were reduced to 2.6% for the mean and 7.8% for the SD. However, ip estimation became much worse with MAREs being 128.9% for the mean and 241.1% for the SD. Overall, storm pattern generation needs improvement. For better storm pattern generation for the region, precipitation depth, D, and rp may be generated correlatively using Copula methods.