Impacts of climate change and land-use change on summer water vapor contribution in eastern China based on a Bayesian isotopic mixing model

气候学 蒸腾作用 环境科学 水蒸气 降水 平流 蒸发 蒸散量 水循环 大气科学 地理 地质学 气象学 化学 物理 生态学 生物化学 光合作用 生物 热力学
作者
Jiacheng Chen,Jie Chen,XC Zhang,Peiyi Peng
出处
期刊:Journal of Climate [American Meteorological Society]
被引量:2
标识
DOI:10.1175/jcli-d-23-0566.1
摘要

Abstract Water vapor transport plays an important role in hydrological processes, influencing water cycles at global and regional scales. Investigating the change in water vapor sources of precipitation is helpful to understand the precipitation change and its cause. Combining the Bayesian isotopic mixing model and the Hybrid Single-Particle Lagrangian Integrated Trajectory model, this study determines the contribution change of water vapor sources for precipitation and the difference of water vapor sources under different land use and cover in eastern China. The study estimated that the mean contributions of advection, evaporation and transpiration vapor to summer precipitation during 1969-2017 are 80.3%, 5.1%, and 14.5%, respectively. Among the advection vapor, vapor from Eurasia and the Western Pacific Ocean contributes most to the precipitation in North China, and vapor from the Indian Ocean, South China Sea and Western Pacific Ocean contributes most in South China. The contribution of advection vapor to precipitation decreases at the rate of 0.7 % decade −1 , while the contributions of evaporation and transpiration vapor increase at the rate of 0.2 % decade −1 and 0.5 % decade −1 , respectively. Advection vapor contribution is the controlling factor of summer precipitation change, while local evaporation and transpiration vapor are also contributors. In addition, the contributions of evaporation and transpiration vapor to precipitation are influenced by land use and cover type. The contributions of evaporation and transpiration vapor to precipitation for large-proportion forests are higher than those for cultivated lands, while the contributions for small-proportion forests are lower than those for cultivated lands.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
彩色如南发布了新的文献求助10
1秒前
1秒前
lemon发布了新的文献求助10
2秒前
千与发布了新的文献求助10
2秒前
耿周周完成签到,获得积分10
2秒前
刘万根发布了新的文献求助10
2秒前
齐天小圣发布了新的文献求助10
2秒前
科目三应助神奇的蘑菇采纳,获得10
3秒前
3秒前
Orange应助山复尔尔采纳,获得10
3秒前
大模型应助空格TNT采纳,获得10
4秒前
南宫清涟应助昏睡的蟠桃采纳,获得10
4秒前
4秒前
Owen应助稳重盼夏采纳,获得10
5秒前
5秒前
量子星尘发布了新的文献求助10
5秒前
6秒前
思源应助Sun采纳,获得10
6秒前
彼岸花开发布了新的文献求助20
6秒前
6秒前
无聊的羊发布了新的文献求助10
7秒前
天天下文献完成签到 ,获得积分10
7秒前
李爱国应助科研通管家采纳,获得10
7秒前
大模型应助科研通管家采纳,获得10
7秒前
斯文败类应助科研通管家采纳,获得50
7秒前
BowieHuang应助科研通管家采纳,获得10
7秒前
7秒前
研友_8QyXr8完成签到,获得积分10
7秒前
传奇3应助科研通管家采纳,获得10
7秒前
脑洞疼应助科研通管家采纳,获得10
8秒前
8秒前
科研通AI6应助科研通管家采纳,获得10
8秒前
SciGPT应助科研通管家采纳,获得10
8秒前
Lucas应助科研通管家采纳,获得10
8秒前
unnamed匿名应助科研通管家采纳,获得10
8秒前
Owen应助科研通管家采纳,获得10
8秒前
376应助科研通管家采纳,获得10
8秒前
8秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 临床微生物学程序手册,多卷,第5版 2000
List of 1,091 Public Pension Profiles by Region 1621
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] | NHBS Field Guides & Natural History 1500
The Victim–Offender Overlap During the Global Pandemic: A Comparative Study Across Western and Non-Western Countries 1000
King Tyrant 720
Sport, Social Media, and Digital Technology: Sociological Approaches 650
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5593281
求助须知:如何正确求助?哪些是违规求助? 4679223
关于积分的说明 14808834
捐赠科研通 4643607
什么是DOI,文献DOI怎么找? 2534406
邀请新用户注册赠送积分活动 1502418
关于科研通互助平台的介绍 1469329