Climate Change Modelling and Its Roles to Chinese Crops Yield

Climate has been changing in the last fifty years in China and will continue to change regardless any efforts for mitigation. Agriculture is a climate-dependent activity and highly sensitive to climate changes and climate variability. Understanding the interactions between climate change and agricultural production is essential for society stable development of China. The first mission is to fully understand how to predict future climate and link it with agriculture production system. In this paper, recent studies both domestic and international are reviewed in order to provide an overall image of the progress in climate change researches. The methods for climate change scenarios construction are introduced. The pivotal techniques linking crop model and climate models are systematically assessed and climate change impacts on Chinese crops yield among model results are summarized. The study found that simulated productions of grain crop inherit uncertainty from using different climate models, emission scenarios and the crops simulation models. Moreover, studies have different spatial resolutions, and methods for general circulation model （GCM） downscaling which increase the uncertainty for regional impacts assessment. However, the magnitude of change in crop production due to climate change （at 700 ppm CO2 eq correct） appears within ±10% for China in these assessments. In most literatures, the three cereal crop yields showed decline under climate change scenarios and only wheat in some region showed increase. Finally, the paper points out several gaps in current researches which need more studies to shorten the distance for objective recognizing the impacts of climate change on crops. The uncertainty for crop yield projection is associated with climate change scenarios, CO2 fertilization effects and adaptation options. Therefore, more studies on the fields such as free air CO2 enrichment experiment and practical adaptations implemented need to be carried out.

CROPGRO-Soybean Model Calibration and Assessment of Soybean Yield Responses to Climate Change

Process-based crop simulation models are useful for simulating the impacts of climate change on crop yields. Currently, estimation of spatially calibrated soil parameters for crop models can be challenging, as it requires the availability of long-term and detailed input data from several sentinel sites. The use of aggregated regional data for model calibrations has been proposed but not been employed in regional climate change studies. The study: 1) employed the use of county-level data to estimate spatial soil parameters for the calibration of CROPGRO-Soybean model and 2) used the calibrated model, assimilated with future climate data, in assessing the impacts of climate change on soybean yields. The CROPGRO-Soybean model was calibrated using major agricultural soil types, crop yield and current climate data at county level, for selected counties in Alabama for the period 1981-2010. The calibrated model simulations were acceptable with performance indicators showing Root Mean Square Error percent of between 27 - 43 and Index of Agreement ranging from 0.51 to 0.76. Projected soybean yield decreased by an average of 29% and 23% in 2045, and 19% and 43% in 2075, under Representative Concentration Pathways 4.5 and 8.5, respectively. Results showed that late-maturing soybean cultivars were most resilient to heat, while late-maturing cultivators needed optimized irrigation to maintain appropriate soil moisture to sustain soybean yields. The CROPGRO-Soybean phenological and yield simulations suggested that the negative effects of increasing temperatures could be counterbalanced by increasing rainfall, optimized irrigation, and cultivating late-maturing soybean cultivars.

Global Climate Model Selection for Analysis of Uncertainty in Climate Change Impact Assessments of Hydro-Climatic Extremes

Regional climate change impact assessments are becoming increasingly important for developing adaptation strategies in an uncertain future with respect to hydro-climatic extremes. There are a number of Global Climate Models (GCMs) and emission scenarios providing predictions of future changes in climate. As a result, there is a level of uncertainty associated with the decision of which climate models to use for the assessment of climate change impacts. The IPCC has recommended using as many global climate model scenarios as possible;however, this approach may be impractical for regional assessments that are computationally demanding. Methods have been developed to select climate model scenarios, generally consisting of selecting a model with the highest skill (validation), creating an ensemble, or selecting one or more extremes. Validation methods limit analyses to models with higher skill in simulating historical climate, ensemble methods typically take multi model means, median, or percentiles, and extremes methods tend to use scenarios which bound the projected changes in precipitation and temperature. In this paper a quantile regression based validation method is developed and applied to generate a reduced set of GCM-scenarios to analyze daily maximum streamflow uncertainty in the Upper Thames River Basin, Canada, while extremes and percentile ensemble approaches are also used for comparison. Results indicate that the validation method was able to effectively rank and reduce the set of scenarios, while the extremes and percentile ensemble methods were found not to necessarily correlate well with the range of extreme flows for all calendar months and return periods.

An Econometric Analysis on the Effect of Climate Change on Wheat Cropping Area in China

[Objective] This study aimed to explore the impact of climate change on wheat cropping by using province-specific historical data during 1996-2007. [Method] We established a panel data econometric model with lagged wheat cropping area and province-specific fixed-effects model to control the unobserved factors. [Result] The results showed that the temperature positively affects wheat cropping area, while precipitation does not have such impact. [Conclusion] The study provided empirical evidence for analysis of the determinants of wheat cropping area in China.

Assessment of Climate Change Impact on Water Requirement and Rice Productivity

Assessing the impact of climate change(CC)on agricultural production systems is mainly done using crop models associated with climate model outputs.This review is one of the few,with the main objective of providing a recent compendium of CC impact studies on irrigation needs and rice yields for a better understanding and use of climate and crop models.We discuss the strengths and weaknesses of climate impact studies on agricultural production systems,with a particular focus on uncertainty and sensitivity analyses of crop models.Although the new generation global climate models(GCMs)are more robust than previous ones,there is still a need to consider the effect of climate uncertainty on estimates when using them.Current GCMs cannot directly simulate the agro-climatic variables of interest for future irrigation assessment,hence the use of intelligent climate tools.Therefore,sensitivity and uncertainty analyses must be applied to crop models,especially for their calibration under different conditions.The impacts of CC on irrigation needs and rice yields vary across regions,seasons,varieties and crop models.Finally,integrated assessments,the use of remote sensing data,climate smart tools,CO_(2)enrichment experiments,consideration of changing crop management practices and multi-scale crop modeling,seem to be the approaches to be pursued for future climate impact assessments for agricultural systems。

Estimation of the potential geographical distribution of a new potato pest(Schrankia costaestrigalis) in China under climate change

Global food security is threatened by the impacts of the spread of crop pests and changes in the complex interactions between crops and pests under climate change.Schrankia costaestrigalis is a newly-reported potato pest in southern China.Early-warning monitoring of this insect pest could protect domestic agriculture as it has already caused regional yield reduction and/or quality decline in potato production.Our research aimed to confirm the potential geographical distributions(PGDs)of S.costaestrigalis in China under different climate scenarios using an optimal MaxEnt model,and to provide baseline data for preventing agricultural damage by S.costaestrigalis.Our findings indicated that the accuracy of the optimal MaxEnt model was better than the default-setting model,and the minimum temperature of the coldest month,precipitation of the driest month,precipitation of the coldest quarter,and the human influence index were the variables significantly affecting the PGDs of S.costaestrigalis.The highly-and moderately-suitable habitats of S.costaestrigalis were mainly located in eastern and southern China.The PGDs of S.costaestrigalis in China will decrease under climate change.The conversion of the highly-to moderately-suitable habitat will also be significant under climate change.The centroid of the suitable habitat area of S.costaestrigalis under the current climate showed a general tendency to move northeast and to the middle-high latitudes in the 2030s.The agricultural practice of plastic film mulching in potato fields will provide a favorable microclimate for S.costaestrigalis in the suitable areas.More attention should be paid to the early warning and monitoring of S.costaestrigalis in order to prevent its further spread in the main areas in China’s winter potato planting regions.

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios: A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.

Potential Impact of Future Climate Change on Crop Yield in Northeastern China

We evaluated the potential impact of future climate change on spring maize and single-crop rice in northeastern China （NEC） by employing climate and crop models. Based on historical data, diurnal temperature change exhibited a distinct negative relationship with maize yield, whereas minimum temperature correlated positively to rice yield. Corresponding to the evaluated climate change derived from coupled climate models included in the Coupled Model Intercomparison Project Phase 5 （CMIP5） under the Representative Concentration Pathway 4.5 scenario （RCP4.5）, the projected maize yield changes for three future periods [2010-39 （period 1）, 2040-69 （period 2）, and 2070-99 （period 3）] relative to the mean yield in the baseline period （1976-2005） were 2.92%, 3.11% and 2.63%, respectively. By contrast, the evaluated rice yields showed slightly larger increases of 7.19%, 12.39%, and 14.83%, respectively. The uncertainties in the crop response are discussed by considering the uncertainties obtained from both the climate and the crop models. The range of impact of the uncertainty became markedly wider when integrating these two sources of uncertainty. The probabilistic assessments of the evaluated change showed maize yield to be relatively stable from period 1 to period 3, while the rice yield showed an increasing trend over time. The results presented in this paper suggest a tendency of the yields of maize and rice in NEC to increase （but with great uncertainty） against the background of global warming, which may offer some valuable guidance to government policymakers.

PREDICTION AND UNCERTAINTY OF CLIMATE CHANGE IN CHINA DURING 21ST CENTURY UNDER RCPS

Based on integrated simulations of 26 global climate models provided by the Coupled Model Intercomparison Project(CMIP), this study predicts changes in temperature and precipitation across China in the 21 st century under different representative concentration pathways(RCPs), and analyzes uncertainties of the predictions using Taylor diagrams. Results show that increases of average annual temperature in China using three RCPs(RCP2.6, RCP4.5,RCP8.5) are 1.87 ℃, 2.88 ℃ and 5.51 ℃, respectively. Increases in average annual precipitation are 0.124, 0.214, and 0.323 mm/day, respectively. The increased temperature and precipitation in the 21 st century are mainly contributed by the Tibetan Plateau and Northeast China. Uncertainty analysis shows that most CMIP5 models could predict temperature well, but had a relatively large deviation in predicting precipitation in China in the 21 st century. Deviation analysis shows that more than 80% of the area of China had stronger signals than noise for temperature prediction;however, the area proportion that had meaningful signals for precipitation prediction was less than 20%. Thus, the multi-model ensemble was more reliable in predicting temperature than precipitation because of large uncertainties of precipitation.

Coupling the 4M crop model with national geo-databases for assessing the effects of climate change on agro-ecological characteristics of Hungary

The 4M crop model was used to investigate the prospective effects of climate change on the agro-ecological characteristics of Hungary.The model was coupled with a detailed meteorological database and spatial soil information systems covering the whole territory of Hungary.Plant-specific model parameters were determined by inverse modeling.Future meteorological data were produced from the present meteorological data by combining a climate change scenario and a stochastic weather generator.Using the available and the generated data,the present and the prospective agro-ecological characteristics of Hungary were determined.According to the simulation results,average yields will decrease considerably(-30%)due to climate change.The rate of nitrate leaching will prospectively decrease as well.The fluctuations of both the yields and the annual nitrate leaching rates will most likely increase approaching the end of the twenty-first century.On the basis of the simulation results,the role of autumn crops is likely to become more significant in Hungary.The achieved results can be generalized for more extended regions based on the concept of spatial(geographical)analogy.

The Impact of Climate Change on Crop Yields in Sub-Saharan Africa

This study estimates of the impact of climate change on yields for the four most commonly grown crops (millet, maize, sorghum and cassava) in Sub-Saharan Africa (SSA). A panel data approach is used to relate yields to standard weather variables, such as temperature and precipitation, and sophisticated weather measures, such as evapotranspiration and the standardized precipitation index (SPI). The model is estimated using data for the period 1961-2002 for 37 countries. Crop yields through 2100 are predicted by combining estimates from the panel analysis with climate change predictions from general circulation models (GCMs). Each GCM is simulated under a range of greenhouse gas emissions (GHG) assumptions. Relative to a case without climate change, yield changes in 2100 are near zero for cassava and range from –19% to +6% for maize, from –38% to –13% for millet and from –47% to –7% for sorghum under alternative climate change scenarios.

Analysis of the Effects of Climate Change on Crop Output in Nigeria

This study investigates the effects of climate change factors and non-climate change factors on crop output in Nigeria. Empirical research approach was adopted with the use of secondary sources of time series annual data obtained from reputable sources for the period 1980-2013. Error Correction Mechanism was used for the analysis. It was found that in the short run, only rainfall tested significantly positive to crop output among the climate change factors but there is evidence of significant effects of all climate change factors on crop output in the long-run. For example, temperature, carbon dioxide emission, carbon emission and rainfall were tested significantly to crop output. Furthermore, non-climate change factors like economically active population, gross capital formation, and land area equipped for irrigation were significantly positive to crop output. To forestall the effects of climate change on crop output, the study recommends that policy makers should formulate policies that will aid farmers towards adaptation practices in farming that can mitigate the effects of climate change. Furthermore, governments and other relevant agencies should also design programmes that can motivate the masses to increase their involvement in crop production.

Uncertainties in assessing the effect of climate change on agriculture using model simulation and uncertainty processing methods

Model simulation is an important way to study the effects of climate change on agriculture.Such assessment is subject to a range of uncertainties because of either incomplete knowledge or model technical uncertainties,impeding effective decision-making to climate change.On the basis of uncertainties in the impact assessment at different levels,this article systematically summarizes the sources and propagation of uncertainty in the assessment of the effect of climate change on agriculture in terms of the climate projection,the assessment process,and the crop models linking to climate models.Meanwhile,techniques and methods focusing on different levels and sources of uncertainty and uncertainty propagation are introduced,and shortcomings and insufficiencies in uncertainty processing are pointed out.Finally,in terms of how to accurately assess the effect of climate change on agriculture,improvements to further decrease potential uncertainty are suggested.

Application of a Full Hierarchical Bayesian Model in Assessing Streamflow Response to a Climate Change Scenario at the Coweeta Basin, NC, USA

We have applied a full hierarchical Baysian （HB） model to simulate streamffow at the Coweeta Basin that drains western North Carolina, USA under a doubled CO2 climate scenario. The full HB model coherently assimilated multiple data sources and accounted for uncertainties from data, parameters and model structures. Full predictive distributions for streamflow from the Bayesian analysis indicate not only increasing drought, with substantial decrease in fall and summer flows, and soil moisture content, but also increase in the frequency of flood events when they were fit with Generalized Extreme Value （GEV） distribution and Generalized Pareto Distribution （GPD） under this doubled CO2 climate scenario compared to the current climate scenario. Full predictive distributions based on the hierarchical Bayesian model, compared to deterministic point estimates, is capable of providing richer information to facilitate development of adaptation strategy to changing climate for a sustainable water resource management.

Assessment of Carbon Dynamics of Forest Ecosystems in the Poyang Lake Basin Responding to Afforestation and Future Climate Change

Afforestation projects were applied in the Poyang Lake Basin of China at the beginning of 1980s. The large-scale plantation may dramatically influence the changes in carbon storage of forests in this basin. Therefore, climate-induced variations in the carbon balance of the Poyang Lake Basin＇s forests may play an important role in the carbon cycle of China. However, we have little understanding of their long-term behavior, especially the future trend of carbon sink/source patterns under climate change and rising atmospheric CQ. The annual carbon budget of the Poyang Lake Basin＇s forests during 1981-2050 was estimated by using the Integrated Terrestrial Ecosystem Carbon-budget model （InTEC） coupled with projected climate change simulated by Regional Integrated Environmental Model System （RIEMS 2.0）. During 1981-2000, the rapid increment of annual NPP in this basin was possible due to large plantation. Soil organic carbon storage （0-30cm） of forests generally decreased by 1.0% per year at the beginning of plantation. Moreover, forests in this basin converted from carbon source in 1980s to carbon sink in 1990s. By 2040-2050, total carbon stocks of forest ecosystems will increase by 0.78Pg C, compared to recent years （2001-2010）. Under future climate and CQ concentration in AIB scenario, NEP of forests in Poyang Lake Basin lean to keep relative stable （20-30Tg C y-i） because of old forests except for some years induced by extreme droughts. Our results also showed that prediction of NEP of forests in Poyang Lake Basin was controlled by water limitation; in contrast, temperature was the main factor on inter- annual variability of NPP.

Climate change in China in the 21st century as simulated by a high resolution regional climate model

Climate change in the 21st century over China is simulated using the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model version 3 (RegCM3). The model is one-way nested within the global model CCSR/NIES/FRCGC MIROC3.2_hires (Center for Climate System Research/National Institute for Environmental Studies/Frontier Research Center for Global Change/Model for Interdisciplinary Research on Climate). A 150-year (1951-2100) transient simulation is conducted at 25 km grid spacing, under the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) A1B scenario. Simulations of present climate conditions in China by RegCM3 are compared against observations to assess model performance. Results show that RegCM3 reproduces the observed spatial structure of surface air temperature and precipitation well. Changes in mean temperature and precipitation in December-January-February (DJF) and June-July-August (JJA) during the middle and end of the 21st century are analyzed. Significant future warming is simulated by RegCM3. This warming becomes greater with time, and increased warming is simulated at high latitude and high altitude (Tibetan Plateau) areas. In the middle of the 21st century in DJF, a general increase of precipitation is found in most areas, except over the Tibetan Plateau. Precipitation changes in JJA show an increase over northwest China and a decrease over the Tibetan Plateau. There is a mixture of positive and negative changes in eastern China. The change pattern at the end of the century is generally consistent with that in mid century, except in some small areas, and the magnitude of change is usually larger. In addition, the simulation is compared with a previous simulation of the RegCM3 driven by a different global model, to address uncertainties of the projected climate change in China.

Simulating the Impacts of Global Warming on Wheat in China Using a Large Area Crop Model

Temperature is one of the most prominent environmental factors that determine plant growth,development, and yield.Cool and moist conditions are most favorable for wheat.Wheat is likely to be highly vulnerable to further warming because currently the temperature is already close to or above optimum.In this study,the impacts of warming and extreme high temperature stress on wheat yield over China were investigated by using the general large area model（GLAM） for annual crops.The results showed that each 1℃rise in daily mean temperature would reduce the average wheat yield in China by about 4.6%-5.7% mainly due to the shorter growth duration,except for a small increase in yield at some grid cells.When the maximum temperature exceeded 30.5℃,the simulated grain-set fraction declined from 1 at 30.5℃to close to 0 at about 36℃.When the total grain-set was lower than the critical fractional grain-set（0.575-0.6）, harvest index and potential grain yield were reduced.In order to reduce the negative impacts of warming, it is crucial to take serious actions to adapt to the climate change,for example,by shifting sowing date, adjusting crop distribution and structure,breeding heat-resistant varieties,and improving the monitoring, forecasting,and early warning of extreme climate events.

Influence of Climate Change on Winter Wheat Growth in North China During 1950-2000

The crop model World Food Studies (WOFOST) was tuned and validated withmeteorological as well as winter wheat growth and yield data at 24 stations in 5 provinces of NorthChina from 1997 to 2003. The parameterization obtained by the tuning was then used to model theimpacts of climate change on winter wheat growth for all stations using long-term weather data from1950 to 2000. Two simulations were made, one with all meteorological data (rainfed) and the otherwithout water stress (potential). The results indicate that the flowering and maturity datesoccurred 3.3 and 3 days earlier in the 1990s than that in the 1960s due to a 0.65℃ temperatureincrease. The simulated rainfed yields show that the average drought induced yields (potential minusrainfed yields) have decreased by 9.7% over the last 50 years. This is to be compared with a 0.02%decrease in yield if the precipitation limit is lifted. Although the precipitation during thegrowing season has decreased over the last 50 years, the drought effects on the rainfed yieldsremained to be practically unchanged as the spring precipitation did not decrease markedly.

Evaluation for the Performance of the CORDEX Regional Climate Models in Simulating Rainfall Characteristics over Mbarali River Catchment in the Rufiji Basin, Tanzania

This study aims to evaluate the performance of the individual Regional Climate Models (RCMs) used in Coordinated Regional Climate Downscaling Experiment (CORDEX) and the ensemble average of the four RCMs to feign the characteristics of the rainfall pattern for the Mbarali River catchment in Rufiji Basin for the period of 1979 to 2005. Statistical analysis for model performance such as Root mean square error, Mean error, Pearson correlation coefficient, Mean, Median, standard deviation and trend analysis are used. In addition to the statistical measure of model performance, the models are tested on their ability to capture the observed annual cycles and interannual variability of rainfall. Results indicated that the RCMs from the CORDEX indicated a better performance to reproduce the rainfall characteristics over Mbarali River catchment in Rufiji Basin. They reproduced fairly the Era Interim annual cycle and inter-annual variability of rainfall. The ensemble average performed better than individual models in representing rainfall over Mbarali River catchment in Rufiji Basin. These suggest that rainfall simulation from the ensemble average will be used for the assessment of the hydrological impact studies over Mbarali River catchment in Rufiji Basin.

基于机器学习和未来气候变化模式的埃塞俄比亚粮食产量预测

对于以农业产业为支柱的埃塞俄比亚,粮食供应和安全对国家安全和人民的生计尤为重要。由于作物生长和气候因素之间的复杂耦合关系,预测气候变化对农作物产量影响具有较大难度,机器学习技术为这种复杂系统变化的预测提供了一种有效途径。本研究利用37个全球气候模式(GCM)的数据以及土壤数据,基于机器学习模型,预测了埃塞俄比亚2021年至2050年5种主要粮食作物在SSP1-2.6、SSP2-4.5和SSP5-8.5情景下的产量变化。经GCM和变量的筛选后,利用梅赫季和贝尔格季中5种主要粮食作物的10个产量数据对直方图梯度提升决策树、极端梯度提升随机森林、轻梯度提升决策树、随机森林、极限树以及K近邻6种机器学习模型进行训练。经过模型评估,选择表现良好的3个模型,采用线性回归算法进行堆叠,然后使用堆叠模型进行预测。研究结果表明,未来30年埃塞俄比亚梅赫季5种主要粮食作物产量变化以增产<2 t·hm^(-2)为主;SSP126情景下的贝尔格季将出现更明显的减产现象,这可能是由于温室效应的减缓降低了CO_(2)的施肥效应。随着人类活动造成的生态环境恶化,研究区农业生产对粮食结构改变和重新分配生产力的需求不断增长,导致农作物生产力向新的适宜地区转移。研究区在SSP126和SSP585情景下将分别因为干旱缓解和温室效应加剧而获得更高的粮食作物生产力。