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基于蒸散的东北地区干湿变化特征及其影响因素分析

Dry-wet variations and influencing factors in Northeast China based on evapotranspiration

  • 摘要: 区域干湿变化是气候驱动下地表水分供需状态的综合体现,分析东北地区的干湿变化特征对理解水资源演变、保障农业生产及维护生态平衡具有重要意义。为提高MOD16A2GF蒸散发数据在东北地区的适用性,揭示区域干湿变化特征,该研究利用2个实测蒸散站点数据和116个气象站点观测数据,对MOD16A2GF数据集实际蒸散(evapotranspiration, ET)和潜在蒸散(potential evapotranspiration, PET)数据进行线性回归矫正。基于矫正后的ET和PET计算了作物缺水指数(crop water stress index, CWSI)以表征地表干湿状况,探讨了东北地区2001—2024年蒸散和干湿条件的时空变化特征及其影响因素。结果表明:1)2001—2024年东北地区ET显著增加,PET和CWSI显著下降,整体趋于湿润化。2)多年平均ET总体呈东高西低的空间分布特征;PET呈现出西南部高、东北部低的分布特征;CWSI整体由西南向东北递减,其中内蒙古东四盟的东南部CWSI最高,干旱最为严重。大部分区域ET呈增加趋势、PET呈减少趋势,区域湿润化趋势明显。3)降水量是影响东北地区干湿变化的关键气象因子。此外,植被生长状况的改善与农业灌溉通过促进实际蒸散发,对缓解水分胁迫、改善地表干湿状况具有重要调节作用。研究结果为农田水分优化配置和干旱风险防控提供科学依据与决策参考。

     

    Abstract: Dry–wet variation is one of the most important indicators to reflect the surface water supply–demand balance under various climates, and stongly influences hydrological processes, agricultural stability, and ecosystem security. Particularly, Northeast China, as the grain production base and ecological barrier, plays a crucial role in national food security and sustainable development. Therefore, it is often required to explore the driving mechanisms of dry–wet variations in this region. This study aims to determine the spatiotemporal variations in evapotranspiration and dry–wet conditions in Northeast China from 2001 to 2024. A systematic analysis was also used to further explore the driving factors using multiple influencing variables. MOD16A2GF evapotranspiration dataset was constructed for the regional dry–wet variations. Evapotranspiration observations were collected from two stations, and meteorological data were from 116 stations. A linear regression method was applied to correct actual evapotranspiration (ET) and potential evapotranspiration (PET). A leave-one-year-out cross-validation was used to evaluate the accuracy and reliability of the parameters. Furthermore, the crop water stress index (CWSI) was calculated for surface dry-wet conditions using ET and PET after correction. The results showed that: 1) MOD16A2GF data showed good better agreement with observed values, in terms of variation trends. Compared with the original data, the root mean square error (RMSE) and Bias were reduced, while the Nash–Sutcliffe efficiency (NSE) was improved, indicating the high data accuracy and reliability. 2) ET values showed a significantly increasing trend from 2001 to 2024, with a growth rate of 3.11 mm/a. Meanwhile, PET and CWSI values showed a significantly decreasing trend, with an annual decline rate of 3.23 mm/a and 0.01, respectively, indicating an overall wetting trend and a gradual alleviation of water stress. 3) In terms of spatial patterns, ET decreased from 800 to 100 mm from east to west. PET and CWS decreased from 1 200 to 500 mm and from 0.9 to 0.1, respectively, from southwest to northeast. The maximum CWSI values, representing the most severe water stress, were observed in the southeastern part of the Eastern Four Leagues in Inner Mongolia. From the perspective of spatial trends, most regions showed increasing ET and decreasing PET, indicating the widespread wetting trend in the study area. Moreover, there were largely overlapped areas with a significant decrease in CWSI, where ET significantly increased, while PET decreased, further confirming the spatial consistency and robustness of the wetting trend. 4) Precipitation was positively correlated with ET and negatively correlated with PET and CWSI. The increase in precipitation significantly promoted actual evapotranspiration and suppressed potential evapotranspiration demand, thus playing a dominant role in the dry-wet variations in Northeast China. In addition, agricultural irrigation increased surface water supply, while vegetation growth regulated the water exchange process between the land surface and the atmosphere. Both driving factors enhanced actual evapotranspiration and alleviated water stress. The regional water resource can be allocated to prevent drought risks in sustainable agriculture under climatic conditions.

     

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