地球科学进展

地球科学进展 ›› 2019, Vol. 34 ›› Issue (3): 275 -287. doi: 10.11867/j.issn.1001-8166.2019.03.0275

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基于多源信息的综合干旱监测研究进展与展望
江笑薇 1, 2( ),白建军 1, 2( ),刘宪锋 1, 2   
  1. 1. 陕西师范大学地理科学与旅游学院，陕西 西安 7100119
    2. 陕西师范大学地理学国家级实验教学示范中心，陕西 西安 7101
  • 收稿日期:2018-11-24 修回日期:2019-01-10 出版日期:2019-03-10
  • 通讯作者: 白建军 E-mail:648762060@qq.com;bjj@snnu.edu.cn
  • 基金资助:
    陕西省自然科学基金面上项目“土壤质地、作物类型及物候期对遥感干旱指数的影响”(编号：2016JM4016);国家自然科学基金项目“融合多要素及其时滞效应的农业干旱综合监测方法研究”(编号：41801333)

Research Progress and Prospect of Integrated Drought Monitoring Based on Multi-source Information

Xiaowei Jiang 1, 2( ),Jianjun Bai 1, 2( ),Xianfeng Liu 1, 2   

  1. 1. College of Geography and Tourism，Shaanxi Normal University，Xi’an 710119，China
    2. National Demonstration Center for Experimental Geography Education，Shaanxi Normal University，Xi’an 710119，China
  • Received:2018-11-24 Revised:2019-01-10 Online:2019-03-10 Published:2019-04-28
  • Contact: Jianjun Bai E-mail:648762060@qq.com;bjj@snnu.edu.cn
  • About author: Jiang Xiaowei （1992-）， female， Tongchuan City， Shaanxi Province， Ph.D student. Reserch areas include remote sensing of resources and environment and agricultural drought. E-mail: 648762060@qq.com | Jiang Xiaowei （1992-）， female， Tongchuan City， Shaanxi Province， Ph.D student. Reserch areas include remote sensing of resources and environment and agricultural drought. E-mail: 648762060@qq.com |Bai Jianjun(1969-), male, Weinan City, Shaanxi Province, Professor. Reserch areas include remote sensing of resources and environment and agricultural drought. E-mail: bjj@snnu.edu.cn
  • Supported by:
    Project supported by the Shaanxi Natural Science Foundation “Effects of soil texture, crop type and phenological period on remote sensing drought index”(No.2016JM4016);The National Natural Science Foundation of China “Study on integrated monitoring method of agricultural drought based on integrating multiple elements and their time delay effect”(No.41801333)
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当前干旱监测已由单一要素向多要素综合方向转变，为了更好地促进综合干旱监测理论和相关模型的发展，全面系统地分析了综合干旱监测的概念内涵，梳理了综合干旱监测模型的构建方法，将其划分为水平衡模型法、线性模型组合法、多变量联合分布函数法、主成分分析法和多源信息数据挖掘法5种。进一步针对当前综合干旱监测存在的挑战与不足，提出了综合干旱监测模型未来应努力发展的方向，即在理论层面上：一是研究干旱内在机理与发生发展过程，明晰干旱影响因素间的关联关系，构建集成多要素的定量干旱综合监测模型；二是增强干旱监测模型的针对性，依据地域、下垫面、生长季等的不同，发展适宜的干旱监测模型；三是针对模型验证难的问题，构建干旱综合监测模型精度验证指标体系。在技术层面上，研究与干旱相关多源信息的集成与融合，提高其综合利用水平，为干旱监测提供丰富的数据支撑和技术保障。

关键词: 干旱, 多源信息, 综合监测模型

At present, drought monitoring has changed from single factor to multi-factor comprehensive direction. In order to better promote the development of comprehensive drought monitoring theory and related models, the conceptual connotation of comprehensive drought monitoring was comprehensively and systematically analyzed, and the construction methods of comprehensive drought monitoring model were sorted out, which were divided into five

methods

Water balance model method, linear model combination method, multi-variable joint distribution function method, principal component analysis method and multi-source information data mining method. Furthermore, in view of the current challenges and shortcomings of integrated drought monitoring, the direction of future development of integrated drought monitoring model was put forward, that is, at the theoretical level: The first is to study the internal mechanism of drought and its occurrence and development process, clarify the relationship among the factors affecting drought, and construct a comprehensive quantitative drought monitoring model integrating multiple factors; The second is to enhance the pertinence of drought monitoring model, develop suitable drought monitoring model according to different regions, underlying surface, growing season, etc.；The third is to construct the precision verification index system of comprehensive monitoring model for drought in view of the difficulty of model validation. At the technical level, the integration and fusion of drought-related multi-source information is studied to improve its comprehensive utilization level and provide abundant data support and technical support for drought monitoring.
Key words: Drought, Multisource information, Integrated monitoring model.

中图分类号: 

图 1 干旱影响因素
Fig. 1 Drought affecting factor
图2 主要干旱综合监测指数发展历程
Fig. 2 The development history of main drought monitoring index
图3 综合干旱监测模型构建流程
Fig. 3 The construction process of Integrated Drought Monitoring Model
表1 主要综合干旱指数
Table 1 The main integrated drought index
方法 指标名称 应用范围 适应性 局限性

水平衡

模型

帕默尔干旱指数（Palmer Drought Severity

Index，PDSI）

气象干旱、

农业干旱

 考虑蒸散发、降水、径流对干旱的影响，从内在机理角度描述干旱 具有一定的时空局限性

地表供水指数（Soil Water Stress Index，

SWSI）

 农业干旱 在PDSI基础上综合了积雪融水、水库蓄水对干旱的影响 参数较多，不易计算
标准降水蒸散指数（Standardized Precipitation Evaporation Index，SPEI）

农业干旱、

水文干旱

多时间尺度，综合蒸散发等

干旱相关影响因素

 参数较多，不易计算
线性组合 美国干旱监测模型（The U.S. Drought Monitor，USDM） 农业干旱 监测国家、州等大尺度干旱 将指标间表述为线性相关关系，难以描述非线性关系，缺乏内在机理性
美国的最佳混合NLDAS（The North American Land Data Assimilation System）的综合干旱指数（Objective Blended NLDAS Drought Index,OBNDI） 农业干旱

监测国家尺度干旱，提高了

USDM监测精度
最大平均干旱指数（Grand Mean Index，GMI） 气象干旱、水文干旱、农业干旱

考虑地表温度因子，用于地区、

小尺度区域干旱监测
微波综合干旱指数（Microwave Integrated Drought Index，MIDI） 农业干旱 基于多传感器微波遥感数据，监测地区短期干旱
旱情综合监测指数（Comprehensive Remote Sensing Drought Monitoring Index，RSI） 农业干旱 综合温度条件指数和植被条件指数，监测地区干旱
联合分布函数 联合干旱指数（Joint Drought Index，JDI） 气象干旱、水文干旱、农业干旱

基于累计降水与径流的综合干旱指数，描述变量间非线性关系。

能够进行短期风险评估

 构建联合分布函数的干旱变量须具有相同边际分布。缺乏内在机理性

多变量标准化干旱指数（Multivariate

Standardized Drought Index，MSDI）

 气象干旱、水文干旱、农业干旱 基于累计降水和土壤水分的综合干旱指数，能够进行短期风险评估
基于标准帕默尔干旱指数（Standardized Palmer Drought Index,SPDI）的综合干旱指数（Standardized Palmer Drought Index-Joint Drought Index ,SPDI-JDI） 气象干旱、水文干旱、农业干旱

具有不同的时间尺度。

能够进行短期风险评估

主成分

分析

 干旱综合指数（Aggregate Drought Index，ADI） 气象干旱、水文干旱、农业干旱 综合多源信息，据研究区地域特点选择不同变量，具有一定的普适性 只能表述线性相关关系，且假设第一主成分表示原数据最大方差

非线性干旱综合指数（Multivariate Drought

Index，MDI）

气象干旱、

农业干旱

基于核熵成分分析（KECA）法，

优化了模型降维过程

 认为熵是信息最大输出，只能描述非线性变量关系
数据挖掘

干旱综合监测指数（Standardized Drought

Index，SDI）

农业干旱、

气象干旱

 半定量半经验干旱监测模型。解决干旱指标时空尺度不一致问题 建模所需指标数据量较大，数据收集较为困难

植被干旱响应指数（Vegetation Drought

Response Index，Veg-DRI）

 农业干旱

用于国家尺度干旱监测。描述植被（生长期）对干旱的响应力。

高空间、时间分辨率

 难以描述非线性关系，缺乏内在机理性
图4 基于多种数据挖掘方法的综合干旱监测模型构建流程图
Fig.4 The construction flow chart of Integrated Drought Monitoring Model based on multiple data mining methods
图5 综合多源信息干旱监测未来研究方向
Fig.5 The future research direction of drought monitoring with integrated multi-source information
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