Evaluating and Extending CLIGEN Precipitation Generation for the Loess Plateau of China 1
Evaluating and Extending CLIGEN Precipitation Generation for the Loess Plateau of China 1
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DOI：
10.1111/j.1752-1688.2008.00296.x
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发表时间：
2009-04
期刊：
影响因子：
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通讯作者：
Jie Chen;X. Zhang;Wenzhao Liu;Zhi Li
中科院分区：
文献类型：
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作者：
Jie Chen;X. Zhang;Wenzhao Liu;Zhi Li
摘要：气候生成器（CLIGEN）在美国被广泛用于生成长期气候情景，供农业系统模型使用。需要评估其在新地区或气候中使用的适用性。其目的是：（1）评估CLIGEN v5.22564最新版本在12个站点生成日、月和年降水深度以及风暴模式（包括风暴持续时间（D）、相对峰值强度（ip））方面的再现性，分布在黄土高原的10个测站的峰值强度（rp）;测试生成D的指数分布和诱导降水深度和D之间期望的秩相关性的无分布方法是否可以改善风暴模式生成。平均绝对相对误差（MAREs）模拟日，月，年，年最大日降水深度在所有12个站分别为3.5，1.7，1.7和5.0%的平均值和5.0，4.5，13.0，和13.6%的标准偏差（SD），分别。模型较好地模拟了月、年降水深度的分布（p > 0.3），但对日降水深度的模拟效果较差。一阶二态马尔可夫链算法足以生成中国黄土高原的降水发生率;但是，它低估了最长的干旱期。CLIGEN生成的风暴模式很差。对于≥10 mm的风暴，它分别将D的平均值和SD低估了− 60.4%和− 72.6%。与D相比，ip和rp的重现性略好。ip的平均和SD的MARE分别为21.0和52.1%，rp为31.2和55.2%。当使用指数分布生成D时，平均值的MARE降至2.6%，SD降至7.8%。然而，ip估计值变得更差，平均MARE为128.9%，SD为241.1%。总体而言，风暴模式生成需要改进。为了更好地生成该地区的风暴模式，可以使用Copula方法相关地生成降水深度、D和rp。
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.