县域小麦渍害立体监测预警机理研究

Waterlogging is the main agricultural meteorological disaster affecting wheat production in the middle and lower reaches of the Yangtze River. The current coarse generic situation is that precipitation as the forecast index of waterlogging damage and only at county scale cannot effectively guide the agricultural disaster reduction. Thus the methods adopted presently can neither accurately reflect the process of waterlogging, nor distinguish the effects between waterlogging stress and other environmental stresses, and also cannot precisely establish the technical system of monitoring and early-warning. In order to solve the above problems, the following research was conducted in the study area of the jianli county in Hubei province. In this research, plot treatment experiment of wheat is processed with the aim of studying the mechanism of the waterlogging of wheat. The soil aeration stress daily Index (SASDI index) is proposed to be taken as the characteristic value，in order to accurately distinguish the effects upon wheat growth and development caused by waterlogging and other environmental disasters. Based on the SASDI index and the data assimilation of the DHSVM model and microwave remote sensing data, the spatial-temporal distribution information of soil ventilation stress can be extracted to realize precise monitoring and early warning of high spatial and temporal resolution of the waterlogging of wheat, meanwhile combined with the remote sensing unsupervised classification and spatial statistics method, a simplified method of early warning of waterlogging was proposed based on SASDI index, guaranteeing that this warning system can be widely promoted in the meteorological departments along the middle and lower reaches of the Yangtze River.

渍害是影响长江中下游地区小麦主要的农业气象灾害，针对目前气象部门以降水为涝渍害预报指标、预报对象为县域、预警结果不能有效指导农业抗灾减灾的现状，为解决渍害现有指标不能准确反映渍害过程、渍害胁迫与其它环境胁迫不能区分等科学问题，以建立县域小麦渍害精准立体监测与预警技术体系提供理论支撑为目的。本课题以湖北省监利县为研究对象，通过小麦渍害处理小区实验，运用统计分析方法，研究小麦渍害致灾机理，首次提出用土壤通气胁迫日指数（SASDI指数）作为特征值，精准区分渍害和其它环境胁迫对小麦生长发育的影响，并以此为指标，运用模型模拟后的同化技术，结合气象数据，实现基于DHSVM模型和微波遥感数据同化的SASDI指数时空分布信息提取，达到小麦渍害高时空分辨率的精准监测与预警的目的，同时运用遥感非监督分类及空间统计法，提出以SASDI指数为指标的渍害精细化预警简化技术，保障预警系统能在长江中下游大面积推广

渍害是影响长江中下游地区小麦主要的农业气象灾害，针对目前气象部门以降水为涝渍害预报指标、预报对象为县域、预警结果不能有效指导农业抗灾减灾的现状，为解决渍害现有指标不能准确反映渍害过程、渍害胁迫与其它环境胁迫不能区分等科学问题，本项目在小麦受渍的致渍机理研究上、大尺度渍害的遥感监测与预警技术上以及模型研究方面有所突破，主要表现在：①构建了基于土壤低氧胁迫为特征量的小麦受渍程度识别模型，即WI指数（Waterlogging Index）大于5.3（“扬麦11”）或者6.0（“郑麦7698”）时WI指数与产量呈负相关，WI指数越大，其产量越低，WI指数能定量地表达小麦整个生长季的受渍程度及对小麦产量的影响，使大尺度渍害的遥感反演成为可能；②运用微波遥感结合累积降水指数的数据融合技术，实现了大尺度渍害的时空动态监测，实现了渍害监测由点到面的突破。通过水云模型，结合Sentinel-1A SAR数据，提取了12 d间隔的土壤表层相对体积含水量空间分布，再以每天的前期降水指数视作具有一定误差的观察数据，运用卡尔曼滤波插值方法，实现了以天为单位土壤表层相对体积含水量空间分布信息提取，结合WI指数小麦受渍程度识别模型，实现了小麦渍害时空分布信息提取。③率先利用简化技术，采用改进型累积降水指数，实现了高空间分辨率的渍害精准预警。在前人研究基础上，对前期累积降雨指数进行改进，提出了考虑气象条件、地形条件和土壤类型等成灾因子影响的作物潜在渍害日指数概念，并得出监利县夏收作物受渍指标；④ 首次运用DHSVM模型，结合CMIP5耦合模式数据，分析了气候变化对小麦渍害的影响。将研究成果，特别是小麦渍害精细化监测预警预报方法，用软件形式实现，设计了农作物农作物潜在渍害精细化预报预警系统和渍害数据库平台系统2套软件系统，为长江中下游地区农业技术人员提供小麦渍害监测预警服务。