| 引用本文: | 荆思佳,肖薇,王晶苑,郑有飞,王伟,刘强,张圳,胡诚.1958—2017年太湖蒸发量年际变化趋势及主控因子.湖泊科学,2022,34(5):1697-1711. DOI:10.18307/2022.0522 |
| Jing Sijia,Xiao Wei,Wang Jingyuan,Zheng Youfei,Wang Wei,Liu Qiang,Zhang Zhen,Hu Cheng.Evaporation variability and its control factors of Lake Taihu from 1958 to 2017. J. Lake Sci.2022,34(5):1697-1711. DOI:10.18307/2022.0522 |
|
| |
|
|
| 本文已被：浏览 2730次 下载 2466次 |
码上扫一扫！ |
|
|
| 1958—2017年太湖蒸发量年际变化趋势及主控因子 |
|
荆思佳,肖薇,王晶苑,郑有飞,王伟,刘强,张圳,胡诚
|
|
1.浙江省衢州市气象局, 衢州 324000;2.南京信息工程大学大气环境中心, 南京 210044;3.中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室, 北京 100101;4.无锡太湖学院苏格兰学院, 无锡 214063
|
|
| 摘要: |
| 湖泊蒸发对气候变化非常敏感, 是水文循环响应气候变化的指示因子, 因此研究湖泊蒸发的控制因素, 对于理解区域水文循环有重要意义. 本文利用太湖中尺度涡度通量网避风港站观测数据校正JRA-55再分析资料, 驱动CLM4.0-LISSS模型, 并利用2012—2017年涡度相关通量数据和湖表面温度数据检验模型模拟蒸发结果, 验证了该模型在太湖的适用性; 估算了1958—2017年间太湖的湖面蒸发量, 并利用Manner-Kendall趋势检验分析了湖面蒸发的变化趋势, 寻找太湖实际蒸发的年际变化的主控因子. 结果如下: 校正后的JRA-55再分析资料模拟的太湖蒸发与观测值之间存在季节偏差, 但是季节偏差在年尺度上相互抵消, 再分析资料可用于年际尺度太湖蒸发变化的模拟; 1958—2017年间太湖蒸发量以1977年为界, 先下降(-3.6 mm/a), 后增加(2.3 mm/a); 多元逐步回归结果表明, 向下的短波辐射是太湖1958—2017年间太湖蒸发变化的主控因子, 向下的长波辐射、气温、比湿也对湖泊蒸发年际变化有一定影响, 但是风速对蒸发量的年际变化影响不大. |
| 关键词: CLM4.0-LISSS模型 太湖 蒸发 主控因子 |
| DOI：10.18307/2022.0522 |
| 分类号: |
| 基金项目:浙江省气象局青年科技项目(2021QN42)和江苏省杰出青年基金项目(BK20220055)联合资助 |
|
| Evaporation variability and its control factors of Lake Taihu from 1958 to 2017 |
|
Jing Sijia1, Xiao Wei2,3, Wang Jingyuan4, Zheng Youfei5, Wang Wei2,3, Liu Qiang2,3, Zhang Zhen2,3, Hu Cheng2,3
|
|
1.Meteorological Bureau of Quzhou City, Zhejiang Province, Quzhou 324000, P. R. China;2.Center on Atmospheric Environment, Nanjing University of Information Science &3.Technology, Nanjing 210044, P. R. China;4.Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, P. R. China;5.Scotland Academy, Wuxi Taihu University, Wuxi 214063, P. R. China
|
| Abstract: |
| Lake evaporation is sensitive to climate change and can indicate the response of hydrological cycle to climate change, therefore investigating the mechanism controlling lake evaporation is vital to understand regional hydrological cycle. In this study, evaporation over Lake Taihu was simulated using the CLM4-LISSS model driven by calibrated Japanese 55-year Reanalysis (JRA-55) data. The model was calibrated against the lake evaporation measured directly with the eddy covariance method from 2012 to 2017. The long-term trend of annual evaporation over Lake Taihu was analyzed using the Mann-Kendall trend test, and the dominant factors controlling the interannual variability were extracted using the multiple stepwise regression method. The results indicated obvious seasonal biases between the lake evaporation simulated using the calibrated JRA-55 reanalysis data as inputs, but the bias was offset at the annual time scale, which suggested that the calibrated JRA-55 reanalysis data can be used to simulate the annual lake evaporation over Lake Taihu. Regarding the historical trend of evaporation over Lake Taihu, the year of 1977 can be viewed as a transition year, while annual lake evaporation decreased from 1958 to 1977 (-3.6 mm/a) and increased after then (2.3 mm/a). The results of multiple stepwise regression revealed that solar radiation was the dominant contributor to the interannual variability of lake evaporation over Lake Taihu during 1958-2017, and sky longwave radiation, air temperature and specific humidity also influenced lake evaporation, with limit effect from wind speed. |
| Key words: CLM4.0-LISSS Lake Taihu evaporation control factors of historical trend |
|
|
|
|
