区域气候模式与作物模型联接的影响评估模拟实验及不确定性分析

利用英国Hadley中心开发的区域气候模式RCMPRECIS(网格分辨率50km×50km),与经过田间试验资料和历史气候资料验证和校准过的CERES系列作物模式相结合,就区域气候模式与作物模式联接的影响评估方法及其不确定性进行了评估。结果表明,相对于大气环流模型来说,区域气候模式与作物模型的结合省去了随机天气发生器的中间环节,减小了不确定性产生的因素。在站点模拟上,该方法在平原地区的模拟效果较好,而山区的模拟效果较差,但如果能用实测天气数据对模拟的天气数据进行验证,模拟效果明显提高。在区域模拟上,该方法可以较好地体现出产量变化的空间分布规律,但由于空间数据的限制,模拟产量与实际产量的偏差较站点水平要大。     

不同空间尺度下的ALMANAC模型验证

ALMANAC模型最早作为EPIC模型的一部分,用于模拟土壤侵蚀导致的土地生产力的下降．它将试验数据的统计过程和作物生长的机理过程结合起来,是一种典型的基于过程模拟的应用型作物生长模型．如能在不同的空间尺度上验证模型的适用性,无疑会大大扩展模型的应用范围．从这一目的出发,利用美国得克萨斯州19个试验田和9个县的玉米和高粱产量资料及其相关的作物、土壤、田问管理等数据,模拟了1998年田间尺度,1989～1998年县级尺度的平均作物产量．模拟结果表明,ALMANAC模型能够很好地模拟两种不同空间尺度的作物产量,其相对误差在田问尺度上分别为8．9％(高粱)和9．4％(玉米),在县级尺度上分别达到2．6％(玉米)和—0．6％(高粱)．该模型在进行产量预测、掌握作物生长动态,指导农业生产管理和土地利用等方面具有很好的应用前景．     

1961—2010年潜在干旱对我国夏玉米产量影响的模拟分析

玉米是我国重要的粮食和饲料作物,旱灾是玉米生产中常见的气象灾害。采用CERES-Maize作物模拟模型,模拟了1961—2010年潜在干旱对我国夏玉米产量影响的时空变化趋势,并分析了其与大气环流因子间的关系,以期了解我国50年来夏玉米受旱的变化情况,并为干旱的研究方法提供一些参考。结果表明:(1)1961—2010年我国夏玉米的潜在产量损失呈略微下降的趋势,不同时期表现不同,其中20世纪60年代、90年代表现为上升趋势。(2)在过去50年里,我国夏玉米潜在旱灾损失中心有向东北移动的趋势,华北地区受旱程度的减轻和东北地区受旱程度的增强是造成损失中心移动的主要原因。(3)我国夏玉米潜在旱灾产量损失中心的经纬度和影响我国夏季降水的北极涡、副热带高压系统的部分指数具有显著的相关关系。当北极涡在生长季前期或同期偏小、偏弱时,我国夏玉米潜在旱灾产量损失中心将偏东、偏北,而副热带高压系统影响更为复杂。     

站点CERES-Rice模型区域应用效果和误差来源

作物区域模拟是利用有限的空间数据,最大限度地反映出生育期、产量等作物性状的时空变化规律.由于目前的作物模型大多是田间尺度的站点模型,把它运用到区域水平的效果如何研究甚少.文章利用CERES-Rice模型,对作物模型在我国的区域应用效果进行了分析.首先利用田间观测数据在各实验点上对模型进行了详细的站点校准,以验证模型在我国的模拟能力;然后以我国水稻生态区(精确到亚区)为单位,运用均方根差(RMSE)法进行了区域校准和验证;最后利用区域校准后的CERES-Rice模型,模拟了1980～2000年的网格(50km×50km)水稻产量,并与同期农调队调查产量进行统计比较,以验证区域应用的效果,为区域模拟的推广和应用提供参考.结果表明:经过空间校准后的CERES-Rice模型,在水稻的主产区1～4区(占种植面积的95%)模拟的平均产量与调查产量相对均方根差在22%以内,两者的符合度也较好,个别区域(5、6) RMSE%在24%～30%之间;1980～2000年水稻各产区模拟的平均产量与调查产量随时间变化趋势也具有一定的一致性;全国1896个网格中,大部分网格(71.01%)模拟的21年水稻年产量与调查产量的RMSE%在30%之内,且大部分分布在水稻主产区,考虑到水稻种植面积的权重后,认为利用区域校准和验证后的CERES-Rice模型进行水稻区域模拟,可以反映出产量的时空分布特征,能够为宏观决策提供相应的信息.但目前区域模拟中还存在着一定的误差,有待今后进一步研究.     

WOFOST模型在内蒙古河套灌区模拟玉米生长全程的适应性

在河套灌区引入成熟的作物模型并进行适应性验证,可为进一步开展玉米生长监测及估产提供依据和基础。本文利用河套灌区巴彦淖尔农业气象试验站2012年玉米观测数据,结合当地气象、土壤资料对荷兰瓦赫宁根大学开发的WOFOST模型进行参数校准,并利用2013年玉米观测数据和2001—2011年农业气象观测资料对模型的区域适用性进行验证,获得了玉米的基本作物参数,包括各发育阶段比叶面积、最大CO2同化率、单叶光能利用率等。结果表明:通过校准作物参数,WOFOST模型可以较好地模拟LAI扩展、生物量的动态积累过程,LAI、各器官生物量及最终产量的模拟值与实测值吻合较好;独立样本检验中,模型模拟LAI的绝对偏差平均值为0.75,叶生物量、茎生物量、贮存器官生物量、地上部总生物量、产量的归一化均方根误差分别为33%、26%、17%、18%和13%;模拟2001—2011年玉米产量的归一化均方根误差为7.5%。参数校准后的模型对LAI、各器官生物量、产量的模拟结果较为符合实际,WOFOST模型能够适用于河套地区玉米生产过程生理、生态因子诊断、评估等。     

基于GIS的黄土丘陵沟壑区作物生产潜力模拟研究

从YIELD模型的来源、输入文件及基本参数,模型中作物生产力计算各个子模型以及计算流程4个方面作了简单的叙述,以黄土丘陵沟壑区典型小流域晋西狼窝沟为例,在地理信息系统（GIS）技术十,应用YILD模型对该流域的作物生产潜力进行了模拟,并从作物类型,地类,耕作措施及气候条件4个方面对影响该流域作物产量的因素进行了分析。结果表明,该模型对不同作物的模拟产量在总体上与实体产量基本相符合,表明模型可以应用于黄土丘陵沟壑区的作物产量模拟之中,对于不同地类来说,坝地的土壤水分和以力条件明显高于梯田和坡耕地,因而坝地的模拟产量地高于梯田和坡地,但三者之间的差距没有实测产量显著,耕作措施是提高作物生产力的有效途径,对地膜覆盖,梯田以及施肥等耕作措施的模拟产量表明,这3种耕作措施均能有效的物生产力;其产量提高率均平均在85％以上,其中以施肥对作物的增产作用最大,增产率高达95％,,这与实测产量资料基本一致;气候条件是影响作物生产的直接因素,模拟结果表明模型对降水量和温度等气候条件十分敏感,不同年份降水量和温度的差异将直接导致作物生产力的显著不同。对YIELD模型的模拟结果分析表明,该模型可以有效地应用于黄土丘陵沟壑区的作物生产潜力研究。     

气候变化对东北地区玉米生产潜力的影响与调控措施模拟——以吉林省为例

本研究以我国吉林省为例,采用5个典型研究站点1981—2010年的气象观测数据、土壤数据、田间管理资料及玉米产量实测值,应用作物生长模型CERES-Maize对5个不同品种玉米生产潜力进行了模拟,在分析气候因素对生产力影响的基础上,模拟、校准与验证遗传参数,实现应对气候变化、提高作物生产力的调控技术模拟,以期指导作物生产.结果表明: 玉米播种-开花、开花-成熟两个生长阶段天数模拟值和单产的模拟值与实际值极为吻合,归一化均方根误差分别为2.96%、3.40%、9.37%,偏离指数范围为-10.6%～15.2%,玉米光温生产潜力模拟值年均为7799.60～12902.83 kg·hm^-2,每10年下降128.6～880.3 kg·hm^-2;相关性分析表明,影响该地区玉米光温生产潜力下降的主导因素是气候变化,即玉米生育期内温度升高造成的生长期缩短和太阳辐射总量的显著下降.据此模拟的主要调控技术分别是改良玉米品种的耐热性与推迟玉米播种期.遗传参数调控模拟结果表明,玉米光温生产潜力随品种敏感参数P5(灌浆期特征参数,指吐丝至生理成熟大于8 ℃的热量时间)值的增大而呈线性增加趋势,P5值每增加10 ℃·d,玉米光温生产潜力提高154.44～261.10 kg·hm^-2.推迟玉米播期模拟结果表明,除梅河口外,敦化、辽源站点在玉米播期推迟5 d时,光温生产潜力增幅最大,分别为0.47%、1.32%;桦甸、榆树站点在玉米播期推迟15 d时,光温生产潜力增幅最大,分别为1.10%、4.06%.     

黄淮海多熟种植农业区作物历遥感检测与时空特征

多熟种植是高强度农业土地利用的重要特征,但由于缺乏在空间和时间上清晰描述农业多熟种植和作物种植历时空分布的数据,使得区域尺度农田生态系统碳动态估计、农田生产力监测与模拟等有很大的不确定性。黄淮海农业区是以冬小麦-夏玉米二熟制为主的我国粮食主产区,冬小麦和夏玉米分别为光合作用途径为C3和C4的作物,已有研究证明如果在估算生态系统生产力时不考虑一年两季作物及其光能利用率的差异则会导致生产力估算结果过低。研究结合农业气象站点地面作物物候观测数据和空间分辨率500m、8d合成的MOD IS时间序列数据,分析研究区二熟制作物的生长过程、物候特征和作物历的空间差异,发展基于EVI和LSWI时间序列曲线检测多熟区各季作物种植历的方法,获取黄淮海农业区空间表述清晰的熟制和各季作物的生长开始与结束时间数据,并应用农业气象站点数据对方法和所获取的作物历数据进行了比较验证。论述的方法和提取的各季作物的作物历时空数据将能够应用于区域尺度农田生产力估算、生物地球化学循环模拟和农业生态系统监测。     

基于作物模型的低温冷害对我国东北三省玉米产量影响评估

以东北三省玉米低温冷害为研究对象,对作物生长过程模式WOFOST进行适当改进,同时对模型在区域上的适应性进行分析、检验,然后利用改进的作物模型实现低温冷害对玉米影响定量分析和动态评估。以1961—2006年共46a平均气温驱动下的模拟产量作为正常年份的产量水平,当年实际气温驱动下的模拟产量跟平均气温驱动下的模拟产量对比,以减产率和气象条件作为灾害严重程度划分的标准,利用数值模拟试验,确定导致减产的主要气象因子及其量值,进而确定农业气象灾害评估指标,在此基础上,进行区域低温冷害影响评估,包括历年典型低温冷害年份影响评估和年代际影响评估。从年代模拟结果来看,近50a来各年代冷害分布大致规律均表现为北部大于南部、东部大于西部地区,即表现为由东北至西南方向呈递减的趋势,冷害造成玉米减产面积及冷害等级各有差别。评估结果基本上可以较好地反应历史实际情况且与前人已有研究成果相一致。     

基于气候适宜度的东北地区春玉米发育期模拟模型

以春玉米的生理生态发育过程为基础,基于作物生理发育时间恒定原理,建立了可推广应用的作物发育期模拟模型.本文充分考虑光、温、水对作物生育进程的综合影响,设计了基于气候适宜度来动态确定作物生理发育日数的算法,为模型的大范围推广应用奠定了基础.利用东北地区农业气象站2009、2010年观测资料对模型进行了分析和验证,模型运行结果与实际观测情况比较吻合,全生育期的均方根误差(root mean square error,简称RMSE)为3.8d,营养生长阶段发育期模拟结果的相关系数在0.84以上,生殖生长阶段发育期模拟结果的相关系数在0.77以上.模型生物学意义明确、精度较高、数据易获、可操作性强,能够在农业气象业务服务中应用于玉米生育进程的模拟与预测.     

Impact of derived global weather data on simulated crop yields

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA‐POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.     

Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near‐term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target.     

Geographic patterns and dynamics of Alaskan climate interpolated from a sparse station record

Data from a sparse network of climate stations in Alaska were interpolated to provide 1‐km resolution maps of mean monthly temperature and precipitation–‐variables that are required at high spatial resolution for input into regional models of ecological processes and resource management. The interpolation model is based on thin‐plate smoothing splines, which uses the spatial data along with a digital elevation model to incorporate local topography. The model provides maps that are consistent with regional climatology and with patterns recognized by experienced weather forecasters. The broad patterns of Alaskan climate are well represented and include latitudinal and altitudinal trends in temperature and precipitation and gradients in continentality. Variations within these broad patterns reflect both the weakening and reduction in frequency of low‐pressure centres in their eastward movement across southern Alaska during the summer, and the shift of the storm tracks into central and northern Alaska in late summer. Not surprisingly, apparent artifacts of the interpolated climate occur primarily in regions with few or no stations. The interpolation model did not accurately represent low‐level winter temperature inversions that occur within large valleys and basins. Along with well‐recognized climate patterns, the model captures local topographic effects that would not be depicted using standard interpolation techniques. This suggests that similar procedures could be used to generate high‐ resolution maps for other high‐latitude regions with a sparse density of data.     

Crop yield response to climate change varies with cropping intensity

Projections of the response of crop yield to climate change at different spatial scales are known to vary. However, understanding of the causes of systematic differences across scale is limited. Here, we hypothesize that heterogeneous cropping intensity is one source of scale dependency. Analysis of observed global data and regional crop modelling demonstrate that areas of high vs. low cropping intensity can have systematically different yields, in both observations and simulations. Analysis of global crop data suggests that heterogeneity in cropping intensity is a likely source of scale dependency for a number of crops across the globe. Further crop modelling and a meta‐analysis of projected tropical maize yields are used to assess the implications for climate change assessments. The results show that scale dependency is a potential source of systematic bias. We conclude that spatially comprehensive assessments of climate impacts based on yield alone, without accounting for cropping intensity, are prone to systematic overestimation of climate impacts. The findings therefore suggest a need for greater attention to crop suitability and land use change when assessing the impacts of climate change.     

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.     

基于ORYZA2000模型的北京地区旱稻适宜播种期分析

确定适宜播种期是制定合理的作物栽培管理方案的关键内容之一。在作物模型ORYZA2000有效性验证的基础上,以北京地区为例,利用该模型结合长期历史气候资料,对确定旱稻适宜播种期做了初步研究。结果表明：在不考虑水分因子条件下,北京地区旱稻297安全播期的范围较广,多年平均为3月26日-6月4日;受温度升高的影响,最早播期有提前趋势,而最晚播种期有延后趋势。在同一年份内,播期不同旱稻的产量也有一定的变化,呈现为先升高而后降低的趋势。播期过早或过晚导致生育期平均温度偏低是影响穗干物质累积且造成减产的主要原因,在适宜的播期范围内才能获得高产。以90%-100%当年最高产量潜力作为适宜播期的产量指标,确定北京地区旱稻297的适宜播期变化在5月11日-5月19日之间,相应的产量变化在6689-7257 kg/hm2范围内。研究方法可为其他地区旱稻的播期研究提供借鉴。     

From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10 degrees N-17 degrees N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel-Guillot-Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10-50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level.     

A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level

Understanding large‐scale crop growth and its responses to climate change are critical for yield estimation and prediction, especially under the increased frequency of extreme climate and weather events. County‐level corn phenology varies spatially and interannually across the Corn Belt in the United States, where precipitation and heat stress presents a temporal pattern among growth phases (GPs) and vary interannually. In this study, we developed a long short‐term memory (LSTM) model that integrates heterogeneous crop phenology, meteorology, and remote sensing data to estimate county‐level corn yields. By conflating heterogeneous phenology‐based remote sensing and meteorological indices, the LSTM model accounted for 76% of yield variations across the Corn Belt, improved from 39% of yield variations explained by phenology‐based meteorological indices alone. The LSTM model outperformed least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) approaches for end‐of‐the‐season yield estimation, as a result of its recurrent neural network structure that can incorporate cumulative and nonlinear relationships between corn yield and environmental factors. The results showed that the period from silking to dough was most critical for crop yield estimation. The LSTM model presented a robust yield estimation under extreme weather events in 2012, which reduced the root‐mean‐square error to 1.47 Mg/ha from 1.93 Mg/ha for LASSO and 2.43 Mg/ha for RF. The LSTM model has the capability to learn general patterns from high‐dimensional (spectral, spatial, and temporal) input features to achieve a robust county‐level crop yield estimation. This deep learning approach holds great promise for better understanding the global condition of crop growth based on publicly available remote sensing and meteorological data.     

Development and assessment of a coupled crop–climate model

It is well established that crop production is inherently vulnerable to variations in the weather and climate. More recently the influence of vegetation on the state of the atmosphere has been recognized. The seasonal growth of crops can influence the atmosphere and have local impacts on the weather, which in turn affects the rate of seasonal crop growth and development. Considering the coupled nature of the crop–climate system, and the fact that a significant proportion of land is devoted to the cultivation of crops, important interactions may be missed when studying crops and the climate system in isolation, particularly in the context of land use and climate change.
To represent the two-way interactions between seasonal crop growth and atmospheric variability, we integrate a crop model developed specifically to operate at large spatial scales (General Large Area Model for annual crops) into the land surface component of a global climate model (GCM; HadAM3). In the new coupled crop–climate model, the simulated environment (atmosphere and soil states) influences growth and development of the crop, while simultaneously the temporal variations in crop leaf area and height across its growing season alter the characteristics of the land surface that are important determinants of surface fluxes of heat and moisture, as well as other aspects of the land-surface hydrological cycle. The coupled model realistically simulates the seasonal growth of a summer annual crop in response to the GCM's simulated weather and climate. The model also reproduces the observed relationship between seasonal rainfall and crop yield. The integration of a large-scale single crop model into a GCM, as described here, represents a first step towards the development of fully coupled crop and climate models. Future development priorities and challenges related to coupling crop and climate models are discussed.