| 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 |
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