摘要
为准确获取山区小流域的降水空间分布及其资源量,采用Kriging插值法对低分辨率卫星数据进行空间降尺度处理,通过长短期记忆网络(Long Short-Term Memory,LSTM)将局部卫星与观测数据进行降水融合,引入前期降水信息加强卫星与观测降水之间的时间相关性,并利用该模型进行流域降水空间分布估计。结果表明:从空间分布来看,融合模型对暴雨中心位置的捕捉更加精确;从降水量来看,所提模型在短时强降水下的探测率和临界成功指数分别为0.60和0.50,能够改善原始低分辨率卫星降水数据,使其更接近实际情况;从雨量站数量来看,融合降水的精度随着站点数量的增加而提高,当站点数量达到某个临界值时,融合降水的精度趋于稳定。Kriging-LSTM模型为准确获取山区小流域的降水资源提供了新思路。
To accurately acquire the spatial distribution and resources of precipitation in small mountainous watersheds,this study employed the Kriging interpolation method for spatial downscaling of low-resolution satellite data.It integrated local satellite and observational data using the long short-term memory(LSTM)network,enhancing the temporal correlation between satellite and observed precipitation by incorporating antecedent precipitation information.This model was further utilized to estimate the spatial distribution of watershed precipitation.The results indicate that,spatially,the fusion model captures the location of rainstorm centers with greater precision.In terms of precipitation amount,the proposed model shows a probability of detection and a critical success index of 0.60 and 0.50,respectively,under short-duration intense rainfall,improving the original low-resolution satellite rainfall data to better approximate actual conditions.As for the number of precipitation stations,the accuracy of the merged precipitation data increases with the number of stations,reaching stability when a critical value of station density is achieved.The Kriging-LSTM model offers a novel approach for precisely acquiring precipitation resources in small mountainous watersheds.
作者
詹昌洵
张挺
蒋嘉伟
ZHAN Changxun;ZHANG Ting;JIANG Jiawei(College of Civil Engineering,Fuzhou University,Fuzhou 350116,China)
出处
《水科学进展》
EI
CAS
CSCD
北大核心
2024年第1期74-84,共11页
Advances in Water Science
基金
国家自然科学基金资助项目(52079032)
福建省水利科技重大项目(MSK202333)。
