Temporal Scaling Characteristics of Sub-Daily Precipitation in Qinghai-Tibet Plateau
文献类型：期刊论文
| 作者 | Ren, Zhihui; Sang, Yan-Fang; Cui, Peng6; Chen, Deliang5; Zhang, Yichi8,9; Gong, Tongliang4,7; Sun, Shao3; Mellouli, Nedra2 |
| 刊名 | EARTHS FUTURE
 |
| 出版日期 | 2024-03-01 |
| 卷号 | 12期号:3页码:e2024EF004417 |
| 关键词 | sub-daily precipitation databases temporal scale natural disasters Qinghai-Tibet Plateau high mountain regions |
| DOI | 10.1029/2024EF004417 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The Qinghai-Tibet Plateau (QTP) is highly susceptible to destructive rainstorm hazards and related natural disasters. However, the lack of sub-daily precipitation observations in this region has hindered our understanding of rainstorm-related hazards and their societal impacts. To address this data gap, a new approach is devised to estimate sub-daily precipitation in QTP using daily precipitation data and geographical information. The approach involves establishing a statistical relationship between daily and sub-daily precipitation based on data from 102 observation sites. This process results in a set of functions with six associated parameters. These parameters are then modeled using local geographical and climatic information through a machine learning algorithm called support vector regression. The results indicated that the temporal scaling characteristics of sub-daily precipitation can be accurately described using a logarithmic function. The uncertainty of the estimates is quantified using the coefficient of variance and coefficient of skewness, which are estimated using a logarithmic and linear curve, respectively. Additionally, the six parameters are found to be closely linked to geographical conditions, enabling the creation of a 1-km parameters data set. This data set can be utilized to quantitatively describe the probabilistic distribution and extract key information about maximum precipitation duration (from 1 to 12 hr). Overall, the findings suggest that the generated parameters data set holds significant potential for various applications, including risk analysis, forecasting, and early warning for rainstorm-related natural disasters in QTP. The innovative method developed in this study proves to be an effective approach for estimating sub-daily precipitation and assessing its uncertainty in ungauged regions. As one of famous hotspots for natural disaster studies on Earth, the Qinghai-Tibet Plateau (QTP) is highly vulnerable to destructive rainstorm hazard and related natural disasters, causing significant damage to property, infrastructure, agriculture, and resulting in extensive loss of life. Short-duration heavy precipitation at sub-daily scales is an important trigger for flash flood, debris flows and other disasters in QTP. However, it is a poorly gauged high mountain region, observed data for sub-daily precipitation is extremely limited. Although there have been several satellite products and reanalysis data for sub-daily precipitation in QTP, their quality has large bias and uncertainty compared to observations. It leaves a large data gap of sub-daily precipitation, hindering the studies of rainstorm-related natural disasters in the region. In this work, we develop a new strategy to quantify the temporal scaling characteristics of sub-daily precipitation, as a basis of temporal downscaling. Then we use the new strategy to generate a parameters data set, to fill the data gap of sub-daily precipitation in QTP. The parameters data set generated provides an effective way to estimate sub-daily precipitation and its uncertainty, which can effectively serve for the rainstorm-related natural disasters study in QTP. A high-resolution gridded parameters data set is generated to estimate sub-daily precipitation and its uncertainty in QTP The temporal scaling characteristics of sub-daily precipitation in QTP is well described by a logarithmic function Spatial heterogeneity in the temporal scaling characteristics of sub-daily precipitation in QTP is closely related to geographical conditions |
| WOS关键词 | DOWNSCALING RAINFALL ; NEURAL-NETWORK ; RANDOM FOREST ; CLIMATE ; MODEL ; MACHINE ; DATASET ; CHINA ; REGION ; DISAGGREGATION |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| 出版者 | AMER GEOPHYSICAL UNION |
| WOS记录号 | WOS:001173859900001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/203327]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 作者单位 | 1.Paris 8 Univ, Artificial Intelligence & Data Semant, ESILV, Devinci Grp, Paris, France 2.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China 3.Tibet Agr & Anim Husb Univ, Water Conservancy Project & Civil Engn Coll, Linzhi, Peoples R China 4.Univ Gothenburg, Dept Earth Sci, Reg Climate Grp, Gothenburg, Sweden 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China 6.Yarlung Zangbo Grand Canyon Water Cycle Monitoring, Linzhi, Peoples R China 7.Minist Emergency Management China, Key Lab Cpd & Chained Nat Hazards, Beijing, Peoples R China 8.Univ Chinese Acad Sci, Beijing, Peoples R China 9.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ren, Zhihui,Sang, Yan-Fang,Cui, Peng,et al. Temporal Scaling Characteristics of Sub-Daily Precipitation in Qinghai-Tibet Plateau[J]. EARTHS FUTURE,2024,12(3):e2024EF004417. |
| APA | Ren, Zhihui.,Sang, Yan-Fang.,Cui, Peng.,Chen, Deliang.,Zhang, Yichi.,...&Mellouli, Nedra.(2024).Temporal Scaling Characteristics of Sub-Daily Precipitation in Qinghai-Tibet Plateau.EARTHS FUTURE,12(3),e2024EF004417. |
| MLA | Ren, Zhihui,et al."Temporal Scaling Characteristics of Sub-Daily Precipitation in Qinghai-Tibet Plateau".EARTHS FUTURE 12.3(2024):e2024EF004417. |
入库方式： OAI收割
来源：地理科学与资源研究所
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