基于WOFOST-HYDRUS耦合模型的玉米遥感估产研究
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摘要
农业生态系统中作物生长与水文循环间相互关系和相互作用的研究有助于提高绿洲农业生产的水资源利用效率和优化灌溉,是缓解农业和生态用水矛盾的有效途径。基于模型耦合的方式构建作物生长-水文模型,可以综合考虑农业生态系统和水循环间的交互作用,能够量化环境条件与作物生长的关系,有助于我们更好地理解绿洲农业生态水文过程,进而为实现水资源的有效管理和节水农业提供参考。同时,基于作物生长-水文耦合模型实现区域作物估产将有助于政府管理者做出正确的决策,进而可以保护地区乃至国家的粮食安全。
位于中国西北干旱区的黑河干流中游盆地是黑河流域经济和农业最发达的地区,然而随着经济发展和人口增长,该地区水资源供需矛盾日益突出。如何提高农业灌溉水利用效率,实现该地区有限水资源的有效利用是该地区可持续发展所需解决的主要问题之一。精确了解作物生长过程中作物需水量和蒸腾量对实现干旱区节水灌溉和合理利用有限的水资源具有非常重要的意义。本研究以双向松耦合方式耦合了作物生长模型WOFOST与包气带水文模型HYDRUS-1D,建立了作物生长-水文耦合模型。该耦合模型可用于量化作物在生长过程中对水的需求量和蒸腾量,并预测在不同环境和气候条件下的作物产量。WOFOST-HYDRUS耦合模型模拟计算所需要的作物生长参数和土壤水文特性参数通过FSEOPT优化程序和全局优化算法SCE-UA来标定。参数标定后WOFOST-HYDRUS耦合模型可以很好地模拟研究区玉米生长过程、土壤水分变化过程和蒸腾量,模型参数符合研究区玉米品种特性和土壤特性。基于标定后的耦合模型,本研究中将根的实际提水与潜在蒸腾的比值作为水利用效率的指示因子来指导灌溉,通过有效减少土壤深层渗漏得到了优化灌溉策略。与实际灌溉策略相比,优化灌溉策略既能保证玉米产量又能实现有效节水。基于优化灌溉策略,本研究中应用全局灵敏度分析方法测试了耦合模型的性能,定量分析了耦合模型参数、气候及环境因素对玉米产量的影响；应用不确定分析方法模拟分析了不确定环境条件下可能的玉米产量。灵敏度分析结果表明,土壤水力参数、地下水埋深、比叶面积、播种时间、初始光利用效率、最大根深、可见光消光系数和蒸腾速率校正因子这8个模型参数对玉米产量变化影响最大,耦合模型不存在过参数化现象。不确定分析结果表明Monte Carlo方法可以用于揭示作物参数和环境参数不确定对玉米产量概率分布的影响及灌溉减少对玉米产量的影响。基于耦合模型结合不同地下水埋深变化和灌溉量变化则可以模拟分析地下水埋深不确定性对玉米产量的影响,其模拟结果显示地下水埋深超过2.0m时玉米对灌溉的需求增大,地下水埋深在2.0m以上时玉米对灌溉的依赖不大,少量灌溉即可保证玉米生长。为了获取玉米种植分布信息,为实现耦合模型区域应用奠定基础,本研究中采用面向对象分类方法结合归一化植被指数(NDVI)时序数据和黑河中游主要农作物(小麦和玉米)物侯期特征,对黑河中游玉米种植分布信息进行了有效的提取。为了测试耦合模型区域应用的效果,采用集合卡尔曼滤波算法同化了叶面积指数(LAI)遥感观测值和耦合模型模拟值,初步实现了张掖绿洲玉米区域估产。
以上研究结果表明,结合了参数优化算法与灵敏度不确定分析方法的WOFOST-HYDRUS耦合模型可以用来指导农业灌溉、预测作物产量和研究作物参数与环境因素对作物产量的影响。结合了NDVI时序分析和物候特征的面向对象分类方法可以有效区分作物类型。结合遥感信息利用集合卡尔曼滤波算法可以实现WOFOST-HYDRUS耦合模型区域应用。
Study on interactions and feedbacks between crop growth and water cycle in agro-ecosystems can provide help for increasing water efficiency of irrigated oasis, optimizing irrigation scheme and alleviating the competition between agricultural water and ecological water. Modeling interactions and feedbacks of relevant processes based on model coupling can help us to properly understand agro-eco-hydrological processes of irrigated oasis and to provide reference for water management and water saving. Realization of crop yield estimation based on coupled crop growth and hydrologic model can provide help for decision makers to ensure regional or national food security.
The middle basin of Heihe River, located in arid region of northwest China, is the most developed area of Heihe river basin. However, with economic development and an increasing population, there is an increasing competition between the limited water resources and the increasing demand for crop irrigation. An increase in water efficiency of agro-ecosystems, especially irrigated agro-ecosystems in arid regions, is an urgent task. Accurate knowledge of water demand and transpiration during crop growth is critical for sustainable water management and water saving. To predict crop yield under different environmental conditions and climatic conditions, the crop growth model, WOFOST, and the vadose zone hydrologic model, HYDRUS-1D, are coupled to quantify water demand and transpiration during crop growth. FSEOPT program and SCE-UA algorithm are used to calibrate crop growth parameters and soil hydraulic parameters. The results show that the simulations agree well with the observations. The related parameter values are reasonable for local crop (maize) characteristics and soil properties in the study field. The calibrated model is then used to evaluate the water balance and to search for a potential, water-saving scheme. The ratio between actual root uptake and potential transpiration is used as an indicative factor to guide irrigation. The simulated results indicate decreasing in deep percolation can save irrigation water. Based on guided irrigation scheme and the coupled model, global sensitivity analysis (SA) method is further applied to study the effect of the coupled model parameters, climatic and environmental conditions on maize yield. The SA analysis results show that8out of33parameters (HYDRUS parameters, ZIT, SLATB1, IDSOW, EFFTB, RDMCR, KDIFIB, CFET) have great effect on output of the coupled model. The SA analysis results also suggest the coupled model has not over-parameterization. An uncertainty analysis (UA) method is used to predict probability of maize yield in uncertain environmental conditions. The UA analysis results indicate that the uncertainty analysis using Monte Carlo method can reveal the risk of a possible loss of maize yield with irrigation decrease and provide the probability of yield in the uncertainty range of crop parameters and environment parameters. The coupled model integrating with various groundwater depth and irrigation schemes can be used to evaluate the uncertainty influence of groundwater depth on maize yield. The results indicate that the irrigation demand of maize increases when groundwater depth more than2.0m. A small amount of irrigation can guarantee maize growth When groundwater depth less than2.0m. An object-oriented classification method integrated with NDVI time series data and crop phenological information is used to extract the planting distribution data of maize in middle basin of Heihe river. To apply the coupled model at the regional scale, the Ensemble Kalman filter (EnKF) is used to assimilate the leaf area index (LAI) data extracted from Lansat-ETM+imagery. As the result of assimilation, region-wide spatial distribution of the maize yield in Zhang Ye Oasis was obtained.
Synthetically, the method of integrating the coupled WOFOST and HYDRUS models with uncertainty analysis and sensitivity analysis can be used for guiding agricultural irrigation, saving water resources, predicting agricultural production and researching effects of the climatic and environmental change on agricultural production. The method of integrating the object-oriented classification method with NDVI time series data and crop phenological information can be used to differentiate varieties of crops. The method of integrating the EnKF with remote sensing information can be used as a useful tool to apply the coupled WOFOST and HYDRUS models at regional scale.
位于中国西北干旱区的黑河干流中游盆地是黑河流域经济和农业最发达的地区,然而随着经济发展和人口增长,该地区水资源供需矛盾日益突出。如何提高农业灌溉水利用效率,实现该地区有限水资源的有效利用是该地区可持续发展所需解决的主要问题之一。精确了解作物生长过程中作物需水量和蒸腾量对实现干旱区节水灌溉和合理利用有限的水资源具有非常重要的意义。本研究以双向松耦合方式耦合了作物生长模型WOFOST与包气带水文模型HYDRUS-1D,建立了作物生长-水文耦合模型。该耦合模型可用于量化作物在生长过程中对水的需求量和蒸腾量,并预测在不同环境和气候条件下的作物产量。WOFOST-HYDRUS耦合模型模拟计算所需要的作物生长参数和土壤水文特性参数通过FSEOPT优化程序和全局优化算法SCE-UA来标定。参数标定后WOFOST-HYDRUS耦合模型可以很好地模拟研究区玉米生长过程、土壤水分变化过程和蒸腾量,模型参数符合研究区玉米品种特性和土壤特性。基于标定后的耦合模型,本研究中将根的实际提水与潜在蒸腾的比值作为水利用效率的指示因子来指导灌溉,通过有效减少土壤深层渗漏得到了优化灌溉策略。与实际灌溉策略相比,优化灌溉策略既能保证玉米产量又能实现有效节水。基于优化灌溉策略,本研究中应用全局灵敏度分析方法测试了耦合模型的性能,定量分析了耦合模型参数、气候及环境因素对玉米产量的影响；应用不确定分析方法模拟分析了不确定环境条件下可能的玉米产量。灵敏度分析结果表明,土壤水力参数、地下水埋深、比叶面积、播种时间、初始光利用效率、最大根深、可见光消光系数和蒸腾速率校正因子这8个模型参数对玉米产量变化影响最大,耦合模型不存在过参数化现象。不确定分析结果表明Monte Carlo方法可以用于揭示作物参数和环境参数不确定对玉米产量概率分布的影响及灌溉减少对玉米产量的影响。基于耦合模型结合不同地下水埋深变化和灌溉量变化则可以模拟分析地下水埋深不确定性对玉米产量的影响,其模拟结果显示地下水埋深超过2.0m时玉米对灌溉的需求增大,地下水埋深在2.0m以上时玉米对灌溉的依赖不大,少量灌溉即可保证玉米生长。为了获取玉米种植分布信息,为实现耦合模型区域应用奠定基础,本研究中采用面向对象分类方法结合归一化植被指数(NDVI)时序数据和黑河中游主要农作物(小麦和玉米)物侯期特征,对黑河中游玉米种植分布信息进行了有效的提取。为了测试耦合模型区域应用的效果,采用集合卡尔曼滤波算法同化了叶面积指数(LAI)遥感观测值和耦合模型模拟值,初步实现了张掖绿洲玉米区域估产。
以上研究结果表明,结合了参数优化算法与灵敏度不确定分析方法的WOFOST-HYDRUS耦合模型可以用来指导农业灌溉、预测作物产量和研究作物参数与环境因素对作物产量的影响。结合了NDVI时序分析和物候特征的面向对象分类方法可以有效区分作物类型。结合遥感信息利用集合卡尔曼滤波算法可以实现WOFOST-HYDRUS耦合模型区域应用。
Study on interactions and feedbacks between crop growth and water cycle in agro-ecosystems can provide help for increasing water efficiency of irrigated oasis, optimizing irrigation scheme and alleviating the competition between agricultural water and ecological water. Modeling interactions and feedbacks of relevant processes based on model coupling can help us to properly understand agro-eco-hydrological processes of irrigated oasis and to provide reference for water management and water saving. Realization of crop yield estimation based on coupled crop growth and hydrologic model can provide help for decision makers to ensure regional or national food security.
The middle basin of Heihe River, located in arid region of northwest China, is the most developed area of Heihe river basin. However, with economic development and an increasing population, there is an increasing competition between the limited water resources and the increasing demand for crop irrigation. An increase in water efficiency of agro-ecosystems, especially irrigated agro-ecosystems in arid regions, is an urgent task. Accurate knowledge of water demand and transpiration during crop growth is critical for sustainable water management and water saving. To predict crop yield under different environmental conditions and climatic conditions, the crop growth model, WOFOST, and the vadose zone hydrologic model, HYDRUS-1D, are coupled to quantify water demand and transpiration during crop growth. FSEOPT program and SCE-UA algorithm are used to calibrate crop growth parameters and soil hydraulic parameters. The results show that the simulations agree well with the observations. The related parameter values are reasonable for local crop (maize) characteristics and soil properties in the study field. The calibrated model is then used to evaluate the water balance and to search for a potential, water-saving scheme. The ratio between actual root uptake and potential transpiration is used as an indicative factor to guide irrigation. The simulated results indicate decreasing in deep percolation can save irrigation water. Based on guided irrigation scheme and the coupled model, global sensitivity analysis (SA) method is further applied to study the effect of the coupled model parameters, climatic and environmental conditions on maize yield. The SA analysis results show that8out of33parameters (HYDRUS parameters, ZIT, SLATB1, IDSOW, EFFTB, RDMCR, KDIFIB, CFET) have great effect on output of the coupled model. The SA analysis results also suggest the coupled model has not over-parameterization. An uncertainty analysis (UA) method is used to predict probability of maize yield in uncertain environmental conditions. The UA analysis results indicate that the uncertainty analysis using Monte Carlo method can reveal the risk of a possible loss of maize yield with irrigation decrease and provide the probability of yield in the uncertainty range of crop parameters and environment parameters. The coupled model integrating with various groundwater depth and irrigation schemes can be used to evaluate the uncertainty influence of groundwater depth on maize yield. The results indicate that the irrigation demand of maize increases when groundwater depth more than2.0m. A small amount of irrigation can guarantee maize growth When groundwater depth less than2.0m. An object-oriented classification method integrated with NDVI time series data and crop phenological information is used to extract the planting distribution data of maize in middle basin of Heihe river. To apply the coupled model at the regional scale, the Ensemble Kalman filter (EnKF) is used to assimilate the leaf area index (LAI) data extracted from Lansat-ETM+imagery. As the result of assimilation, region-wide spatial distribution of the maize yield in Zhang Ye Oasis was obtained.
Synthetically, the method of integrating the coupled WOFOST and HYDRUS models with uncertainty analysis and sensitivity analysis can be used for guiding agricultural irrigation, saving water resources, predicting agricultural production and researching effects of the climatic and environmental change on agricultural production. The method of integrating the object-oriented classification method with NDVI time series data and crop phenological information can be used to differentiate varieties of crops. The method of integrating the EnKF with remote sensing information can be used as a useful tool to apply the coupled WOFOST and HYDRUS models at regional scale.
引文
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