基于ERA5-Land产品的黄河流域蒸散时空变化特征

doi:10.11755/j.issn.1006-7639(2023)-03-0390

摘要/Abstract

摘要：

近年来，黄河流域气候发生明显变化，对流域地表水文、生态等过程产生显著影响。研究黄河流域蒸散时空变化特征，对加深陆-气相互作用认识及区域水资源管理有重要意义。本文分别在黄河源区、河套地区以及黄河下游地区选取一个代表性站点[海北、SACOL（Semi-Arid Climate and Environment Observatory of Lanzhou University）和禹城站]的观测资料，检验欧洲中期天气预报中心ERA5-Land产品中蒸散资料在黄河流域的适用性。在此基础上，利用ERA5-Land资料中1980—2021年逐月潜热通量资料，结合经验正交函数分解（Empirical Orthogonal Function，EOF）、功率谱和回归分析研究近42 a黄河流域蒸散时空变化特征。结果表明：ERA5-Land资料能够反映海北、SACOL和禹城站蒸散变化特征，与观测资料的相关性较好，偏差和均方根误差较小，适用于黄河流域蒸散时空变化特征分析。黄河流域不同区域蒸散存在多时间尺度变化，显著振荡周期主要为5、15 a，有明显的年际和年代际变化。黄河流域不同区域年蒸散EOF分解第1模态表现出同位相变化，在2004年前后由增大转为减少趋势;第2模态为偶极子分布，空间分布表现反向变化特征。近42 a黄河流域年蒸散为明显减少趋势，不同区域减幅不同，下游减少速率最快，为-3.74 mm·a^-1，河套地区为-2.82 mm·a^-1，黄河源区减少速率相对平缓。夏季蒸散变率最大，河套和下游减少速率较大;冬季蒸散变率较小，黄河源区减少速率最大，为-0.48 mm·a^-1。

关键词: 黄河流域不同区域, 蒸散时空变化, ERA5-Land资料

Abstract:

The climate in the Yellow River Basin has undergone significant changes in recent years, which has a significant impact on surface hydrological and ecological processes in the basin. Studying the spatial and temporal variation of evapotranspiration in the Yellow River Basin is indicative for understanding deeply land-atmosphere interactions and regional water resources management. In this paper, the appliability of ERA5-Land evapotranspiration in the Yellow River Basin was evaluated using in-situ observations of Haibei, SACOL (Semi-Arid Climate and Environment Observatory) and Yucheng stations which are selected as representative stations from the source region, Hetao region and the lower reach of the Yellow River Basin, respectively. Then based on monthly latent heat flux from ERA5-Land data, the spatial and temporal variation of evapotranspiration in the Yellow River Basin in the past 42 years (1980-2021) are analyzed using EOF (Emipirical Orthogonal Function), power spectrum and regression analysis methods. The results show that ERA5-Land data can reflect the variation characteristics of evapotranspiration at Haibei, SACOL and Yucheng stations with good correlation and small error and root mean square deviation, which is applicable for the analysis on spatial and temporal variation of evapotranspiration in the Yellow River Basin. There are multi-timescale variations of evapotranspiration in different regions of the Yellow River Basin, with significant oscillation periods of main 5 a and 15 a, and obvious inter-annual and inter-decadal variations. The first mode in different regions of the Yellow River Basin characterizes the consistency in spatial distribution, which decreases around 2004. The second mode is dipole distribution, indicating the reverse change in space. The deceleration of evapotranspiration in the Yellow River Basin in the past 42 years is not same in different regions, with the fastest rate of -3.74 mm·a^-1 in the lower reaches and -2.82 mm·a^-1 in the Hetao area, while the deceleration in the source area is relatively gentle. The summer evapotranspiration variability is the largest, and the deceleration is faster in the Hetao area and the lower reaches. The winter evapotranspiration variability is smaller, but the source area has the largest winter evapotranspiration deceleration of -0.48 mm·a^-1.

Key words: different regions of the Yellow River Basin, spatio-temporal variation of evapotranspiration, ERA5-Land data

中图分类号:

P426.2+2

杨扬, 王丽娟, 黄小燕, 齐月, 谢蕊. 基于ERA5-Land产品的黄河流域蒸散时空变化特征[J]. 干旱气象, 2023, 41(3): 390-402.

YANG Yang, WANG Lijuan, HUANG Xiaoyan, QI Yue, XIE Rui. Analysis on spatio-temporal variation of evapotranspiration in the Yellow River Basin based on ERA5-Land products[J]. Journal of Arid Meteorology, 2023, 41(3): 390-402.

图/表 8

图1 黄河流域区域划分（绿色框）及1961—2018年平均年降水量空间分布（填色区，单位：mm）（蓝色实线代表黄河。下同）

Fig.1 The regional division （green boxs） of the Yellow River Basin and spatial distribution of average annual precipitation （color filled areas， Unit： mm） from 1961 to 2018 （The blue solid line represents the Yellow River. the same as below）

图2 2003—2010年海北（a、d）和禹城（c、f）站及2007—2012年SACOL站（b、e）ERA5-Land蒸散与观测逐月变化（a、b、c）及散点图（d、e、f）

Fig.2 Monthly variation （a， b， c） and scatter plots （d， e， f） of evapotranspiration between ERA5-Land data and the observation at Haibei （a， d） and Yucheng （c， f） station from 2003 to 2010， SACOL station （b， e） from 2007 to 2012

图3 1980—2021年黄河流域年和季节平均蒸散的空间分布（单位：mm）（a）全年，（b）春季，（c）夏季，（d）秋季，（e）冬季

Fig.3 The spatial distribution of annual and seasonal mean evapotranspiration in the Yellow River Basin during 1980-2021 （Unit： mm）（a） the whole year，（b） spring，（c） summer，（d） autumn，（e） winter

图4 1980—2021年黄河流域不同区域年和不同季节蒸散年际变化（b、d、f、h）及变化趋势空间分布（a、c、e、g）（单位：mm·a-1）（a、b）全年，（c、d）春季，（e、f）夏季，（g、h）秋季（黑点区域表示通过95%的置信水平。下同）

Fig.4 Inter-annual variation (b, d, f, h) of evapotranspiration and spatial distribution of variation trend (a, c, e, g) (Unit: mm·a-1) in the whole year and different seasons in the different area of the Yellow River Basin during 1980-2021 (a, b) annual, (c, d) spring, (e, f) summer, (g, h) autumn(The black dot areas passed the confidence level at 95%. the same as below)

图5 1980—2021年黄河源区年蒸散EOF分解前2个模态空间型（a、b）、标准化时间系数（c、d）及其功率谱（e、f）

Fig.5 Spatial patterns （a， b）， standardized time coefficients （c， d） and their power spectrum （e， f） of the first two modes of EOF analysis of annual evapotranspiration in source region of the Yellow River Basin during 1980-2021

图6 1980—2021年河套地区年蒸散EOF分解前2个模态空间型（a、b）、标准化时间系数（c、d）及其功率谱（e、f）

Fig.6 Spatial patterns （a， b）， standardized time coefficients （c， d） and their power spectrum （e， f） of the first two modes of EOF analysis of annual evapotranspiration in Hetao area during 1980-2021

图7 1980—2021年黄河下游年蒸散EOF分解前2个模态空间型（a、b）、标准化时间系数（c、d）及其功率谱（e、f）

Fig.7 Spatial patterns （a， b）， standardized time coefficients （c， d） and their power spectrum （e， f） of the first two modes of EOF analysis of annual evapotranspiration in the lower reaches of the Yellow River Basin during 1980-2021

图8 基于GLEAM（a、b）、GLDAS（c、d）及Eta（e、f）资料的黄河流域不同区域蒸散年际变化（b、d、f）及变化趋势空间分布（a、c、e）（单位：mm·a-1）

Fig.8 Inter-annual variation (b, d, f) of evapotranspiration and variation trend spatial distribution (a, c, e) (Unit: mm·a-1) in different areas of the Yellow River Basin based on GLEAM (a, b)、GLDAS (c, d) and Eta (e, f) data

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