The effect of temporal aggregation of weather input data on crop growth models’ results

Weather data are essential inputs for crop growth models, which are primarily developed for field level applications using site-specific daily weather data. Daily weather data are often not available, especially when models are applied to large regions and/or for future projections. It is possible to generate daily weather data from aggregated weather data, such as average monthly weather data, e.g. through a linear interpolation method. But, due to the nonlinearity of many weather-crop relationships, results of simulations using linearly interpolated data will deviate from those with actual (daily) data. The objective of this study was to analyse the sensitivity of different modelling approaches to the temporal resolution of weather input data. We used spring wheat as an example and considered three combinations of summarized and detailed approaches to model leaf area index development and associated radiation interception and biomass productivity, reflecting the typical range of detail in the structure of most models. Models were run with actual weather data and with aggregated weather data from which day-to-day variation had been removed by linear interpolation between monthly averages.Results from different climatic regions in Europe show that simulated biomass differs between model simulations using actual or aggregated temperature and/or radiation data. In addition, we find a relationship between the sensitivity of an approach to interpolation of input data and the degree of detail in that modelling approach: increasing detail results in higher sensitivity. Moreover, the magnitude of the day-to-day variability in weather conditions affects the results: increasing variability results in stronger differences between model results. Our results have implications for the choice of a specific approach to model a certain process depending on the available temporal resolution of input data.     

黑龙江省热量资源变化及其对作物生产的影响

气候变暖背景下,热量资源变化势必对寒地农作物生产环境、生长发育及种植制度产生重要影响。本文利用黑龙江省1971—2014年67个观测站逐日气象资料,计算了≥10℃活动积温和≥0℃活动积温(以下简称积温)及无霜期等农业热量指标,采用线性倾向率、累计距平、M-K检验和经验正交函数(EOF)方法等统计方法,分析了热量资源变化特征及突变特征,以及对农业生产的可能影响。结果表明:≥10℃积温和≥0℃积温分别以86.7℃?d?(10a)-1和80.5℃?d?(10a)-1的速率显著增加,无霜期呈延长趋势[倾向率为3.8 d·(10a)-1];≥10℃积温和无霜期在1993年发生突变,突变后二者初日提前,终日延后。≥10℃积温和≥0℃积温的增加幅度西部大于东部,无霜期延长幅度中西部大于东北部,农业热量资源变化幅度大的地区亦是热量敏感区域。热量资源增加对农业的影响,表现在农作物适宜生育期延长;适宜水稻和玉米种植的区域向北、向西扩张,大豆种植重心北移;原适宜种植极早熟、早熟品种的区域逐步被中熟、中晚熟品种替换。热量增加使水稻、玉米和大豆三大作物产量的进一步提高成为可能。     

遥感与作物生长模型数据同化应用综述

遥感是获取大面积地表信息最有效的手段,在农业资源监测、作物产量预测中发挥着不可替代的重要作用;作物生长模型能够实现单点尺度上作物生长发育的动态模拟,可对作物长势以及产量变化提供内在机理解释。遥感信息和作物生长模型的数据同化有效结合二者优势,在大尺度农业监测与预报上具有巨大的应用潜力。该文系统综述了遥感与作物生长模型的同化研究,概述了遥感与作物生长模型数据同化系统的构建,在归纳国内外研究进展的基础上,总结了当前主流同化方法的特点以及在不同条件下的同化效果。进而具体分析影响同化精度的关键环节,明确了相关科学概念,并相应指出改善精度的策略或者方向。最后从多参数协同、多数据融合、动态预测、多模型耦合以及并行计算环境5个方面展望了遥感与作物生长模型数据同化的未来研究重点和发展趋势,同时结合农业应用现实需求,介绍一种数据同化与集合数值预报结合的应用框架,为大区域、高精度同化研究提供新的思路与借鉴。     

Simulating dynamic crop growth with an adapted land surface model - JULES-SUCROS: Model development and validation

The increasing demand for ecosystem services, in conjunction with climate change, is expected to significantly alter terrestrial ecosystems. In order to evaluate the sustainability of land and water resources, there is a need for a better understanding of the relationships between crop production, land surface characteristics and the energy and water cycles. These relationships are analysed using the Joint UK Land Environment Simulator (JULES). JULES includes the full hydrological cycle and vegetation effects on the energy, water, and carbon fluxes. However, this model currently only simulates land surface processes in natural ecosystems. An adapted version of JULES for agricultural ecosystems, called JULES-SUCROS has therefore been developed. In addition to overall model improvements, JULES-SUCROS includes a dynamic crop growth structure that fully fits within and builds upon the biogeochemical modelling framework for natural vegetation. Specific agro-ecosystem features such as the development of yield-bearing organs and the phenological cycle from sowing till harvest have been included in the model. This paper describes the structure of JULES-SUCROS and evaluates the fluxes simulated with this model against FLUXNET measurements at 6 European sites. We show that JULES-SUCROS significantly improves the correlation between simulated and observed fluxes over cropland and captures well the spatial and temporal variability of the growth conditions in Europe. Simulations with JULES-SUCROS highlight the importance of vegetation structure and phenology, and the impact they have on land-atmosphere interactions.     

作物生长模型与定量遥感参数结合研究进展与展望

作物生长模型与定量遥感参数的结合,不仅满足前者实现区域应用的需求,也有助于提高后者的反演精度,在生态、农业、资源调查与全球气候变化等研究上意义重大。该文从作物生长模型空间应用拓展的角度,对国内外主流作物生长模型、定量遥感参数以及两者结合的参数与方法进行了概述,分析了典型作物生长模型的主要模拟过程及其驱动、初始化、输出等参数,总结了当前定量遥感正反演结果可为作物生长模型区域应用提供的参数数据;建立了作物生长模型模拟过程与定量遥感参数的对应关系,对比分析了作物生长模型与定量遥感参数的不同结合方式。基于以上内容,对作物生长模型面应用的限制因素及其与定量遥感参数的关系、作物生长模型面应用时参数尺度效应的影响、作物生长模型与定量遥感参数耦合方法的发展3个方面展开了讨论,以期为作物生长模型与定量遥感参数开展更好的结合研究提供参考。     

基于遥感信息与作物模型集合卡尔曼滤波同化的区域冬小麦产量预测

区域作物产量预测是国家粮食安全评估的重要内容。遥感虽能获取大面积地表信息,却难以反映作物生长发育的内在过程。作物生长模型已经在单点尺度能成功模拟作物的生长发育过程,但是区域尺度作物关键参数的获取仍很困难。遥感信息与作物模型结合的数据同化已经成为区域产量预测的最有效途径。该文选择河北省衡水地区冬小麦为研究对象,在WOFOST模型标定与区域化的基础上,利用WOFOST模型描述冬小麦生育期内叶面积指数(LAI)变化规律。针对MODIS数据的混合像元造成反演的LAI产品偏低的系统误差,利用实测LAI样本点融合MODIS-LAI趋势信息修正MODIS-LAI数据产品。采用集合卡尔曼(EnKF)算法同化冬小麦返青到抽穗期的MODIS-LAI与WOFOST模拟的LAI以获得时间序列最优的LAI,并以此重新驱动WOFOST模型估算区域冬小麦产量。结果表明,EnKF同化后的冬小麦产量比未同化的产量预测精度有显著提高,与县平均统计产量相比,在潜在模式下,决定系数由0.13提高到0.38,均方根误差由2480下降到880kg/hm2。研究表明,遥感信息与作物模型的EnKF同化是1种有效的作物产量预测方法,并在区域尺度应用上具有广阔的应用潜力。该研究可为区域尺度的作物估产提供参考。