基于GRACE数据重建的黄河流域实际蒸散发及其时空演变特征分析
Analysis of actual evapotranspiration and its spatio-temporal evolution characteristics in the Yellow River Basin based on GRACE data reconstruction
王芊予 1粟晓玲 1褚江东 1胡雪雪 1张特1
作者信息
- 1. 西北农林科技大学旱区农业水土工程教育部重点实验室,陕西杨凌 712100;西北农林科技大学水利与建筑工程学院,陕西杨凌 712100
- 折叠
摘要
利用多种深度学习方法对重力场恢复与气候试验(GRACE)数据进行插补,利用随机森林算法对GRACE数据进行空间降尺度,基于水量平衡方程计算黄河流域实际蒸散发,并采用4种蒸散发产品进行验证,进而分析黄河流域实际蒸散发的时空演变规律.结果表明:长短期记忆神经网络的整体插补精度优于深度神经网络和卷积长短期记忆神经网络;基于GRACE数据估算的实际蒸散发与4种蒸散发产品的平均相关系数为0.903,表明基于GRACE数据估算的实际蒸散发结果适用性较好;2003-2021年黄河流域多年平均实际蒸散发为144.38～775.62 mm,空间上呈南多北少的分布特征,时间上呈夏多冬少的季节变化特征,2003-2016年以2.51mm/a的速率上升,2017年后呈下降趋势.
Abstract
Multiple deep learning methods were used to interpolate gravity recovery and climate experiment(GRACE)data,and the random forest algorithm was used to spatially downscale GRACE data.The actual evapotranspiration in the Yellow River Basin was calculated based on the water balance equation.And the data were verified using four evapotranspiration products to analyze the spatio-temporal evolution of actual evapotranspiration in the Yellow River Basin.The results indicate that the overall interpolation accuracy of the long short-term memory neural network is superior to that of deep neural network and convolutional long short-term memory neural network.The average correlation coefficient between the actual evapotranspiration estimated based on GRACE data and four evapotranspiration products is 0.903,indicating that the applicability of the actual evapotranspiration results estimated based on GRACE data is good.The average annual actual evapotranspiration in the Yellow River Basin from 2003 to 2021 was 144.38 to 775.62 mm,with a spatial distribution pattern of more in the south and less in the north,and a seasonal variation pattern of more in summer and less in winter.From 2003 to 2016,it increased at a rate of 2.51 mm/a,and showed a downward trend after 2017.
关键词
实际蒸散发/GRACE数据/深度学习方法/随机森林算法/黄河流域Key words
actual evapotranspiration/GRACE data/deep learning method/random forest algorithm/Yellow River Basin引用本文复制引用
基金项目
国家自然科学基金项目(52079111)
中央高校基本科研业务费专项资金资助项目(2023HHZX004)
出版年
2024
国家科技期刊平台
NSTL
万方数据