基于MODIS和CLDAS的综合干旱监测模型研究
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TP181;S423;P426.616

基金项目:

国家重点研发计划(2018YFC1506602);国家自然科学基金重点项目(91437220)


Integrated drought monitoring model based on MODIS and CLDAS
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    摘要:

    传统的干旱监测指数主要考虑单一影响因子,往往无法全面综合反映干旱状况.基于MODIS数据和CLDAS数据,选取多个影响因子和能够直接反映干旱程度的干旱指数作为自变量,以综合气象干旱指数(CI)为因变量,通过梯度提升机(GBM)机器学习算法建立日尺度综合干旱监测模型,并以2015—2018年华北地区干旱为例进行了研究.结果表明模型监测结果与站点CI计算值具有显著的相关性,训练集和测试集决定系数分别达到0.945和0.655,均方根误差(RMSE)分别为0.033和0.082,综合干旱监测模型具有较高的精度.且模型监测与CI监测各月等级一致率均在65%以上,并与标准化降水蒸散指数(SPEI)和土壤相对湿度(RSM)相关系数分别为0.68和0.60,能较好地反映气象干旱和农业干旱状况.典型干旱情况监测表明,综合干旱监测模型综合考虑多种干旱影响因素,能较准确地识别出干旱的发生,表征综合干旱发生状况.

    Abstract:

    Traditional drought indices mainly consider a single factor and often cannot comprehensively reflect the drought condition.Based on data of MODIS and CLDAS (CMA Land Data Assimilation System),a daily scale integrated drought monitoring model was established by Gradient Boosting Machine (GBM) with multiple influencing factors and drought index as independent variables and comprehensive meteorological drought index (CI) as dependent variable.It was researched by taking drought in North China from 2015 to 2018 as a case.The results show that the model monitoring results are significantly correlated with the calculated CI values of the observation stations.The coefficient of determination is 0.945 and 0.655,and the Root Mean Square Error (RMSE) is 0.033 and 0.082 for training and test sets,respectively,indicating the high accuracy of the proposed integrated drought monitoring model.The consistency rate between the model monitored CI and calculated CI values is above 65%,and the correlation coefficient with Standard Precipitation Evapotranspiration Index (SPEI) and Relative Soil Moisture (RSM) is 0.68 and 0.6,respectively,showing its capacity to reflect both the meteorological drought and the agricultural drought.Monitoring of typical drought condition shows that the integrated drought monitoring model can accurately identify the drought occurrence,and represent the situation of comprehensive drought via considering various drought influencing factors.

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邢雅洁,沈润平,黄安奇,梁宇靖,王云宇,谢昭颖,师春香,孙帅.基于MODIS和CLDAS的综合干旱监测模型研究[J].南京信息工程大学学报(自然科学版),2024,16(3):394-404
XING Yajie, SHEN Runping, HUANG Anqi, LIANG Yujing, WANG Yunyu, XIE Zhaoying, SHI Chunxiang, SUN Shuai. Integrated drought monitoring model based on MODIS and CLDAS[J]. Journal of Nanjing University of Information Science & Technology, 2024,16(3):394-404

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历史
  • 收稿日期:2023-02-28
  • 在线发布日期: 2024-06-15
  • 出版日期: 2024-05-28

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