摘要
为更准确地评价海河流域降水资源量及其空间分布,将海河流域划分为山区迎风坡、山区背风坡和平原区3个片区,采用人工神经网络叠加一致性修正分片区进行多源数据融合校正,生成海河流域2001—2019年降水融合数据集,并利用该融合数据集对流域降水资源进行全面评价。结果表明:从降水数量来看,原始卫星产品在海河流域高估降水,融合校正数据集精度较原始卫星降水数据有较大提升,得到海河流域2001—2019年的年均降水量为515.2 mm,合降水资源量1639.4亿m 3;从降水分布来看,融合校正数据集能更好地捕捉降水空间分布,揭示流域东北、东南、西南和中部偏西降水较多,西北部和中部偏东降水较少;从降水规律来看,海河流域平原区降水与空间位置参数存在很明显的联系,流域山区迎风坡和背风坡降水均与高程变化具有明显的关系。
To increase the robustness of the estimation of Haihe River basin precipitation resources,we divided the basin into three regions:windward mountainside,leeward mountainside,and plains.Assistant by the artificial neural network machine learning and consistency correction principle,we constructed a comprehensive multisource fusion dataset of Haihe River basin precipitation from 2001 to 2019.The results showed that the original satellite precipitation products overestimated precipitation in the basin.The fusion dataset present that the average precipitation of the Haihe River basin from 2001 to 2019 was 515.2 mm,and the rainfall resources were 163.94 billion m 3,respectively.The evaluation parameter of the fusion dataset represented a significant improvement in the accuracy of precipitation estimation.The fusion dataset also better explored the spatial distribution of precipitation in the basin and showed that more rainfall was collected in the northeastern,southeastern,southwestern,and west-central regions of the basin,whereas less was collected in the northwestern and east-central areas.Precipitation over the plains regions of the basin showed a distinct spatial pattern,and precipitation on both windward and leeward mountainsides was related to elevation.
作者
石羽佳
王忠静
索滢
SHI Yujia;WANG Zhongjing;SUO Ying(Department of Hydraulic Engineering,Tsinghua University,Beijing 100084,China;Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China,Ningxia University,Yinchuan 750021,China)
出处
《水科学进展》
EI
CAS
CSCD
北大核心
2022年第4期602-613,共12页
Advances in Water Science
基金
国家重点研发计划资助项目(2021YFD1900600,2022YFE0101100)。
关键词
降水资源
多源数据
融合校正
人工神经网络
海河流域
precipitation resources
multisource data
fusion
artificial neural network
Haihe River basin
