![apsim source code apsim source code](https://businessdocbox.com/docs-images/78/77307144/images/6-1.jpg)
#Apsim source code archive#
Using spatial interpolation to construct a comprehensive archive of Australian climate data. Report to the International Commission for Solar Research on actinometric investigations of solar and atmospheric radiation. On the effective number of climate models. Climate model genealogy: generation CMIP5 and how we got there.
![apsim source code apsim source code](https://www.mdpi.com/plants/plants-10-00443/article_deploy/html/images/plants-10-00443-g002.png)
Different uncertainty distribution between high and low latitudes in modelling warming impacts on wheat. How well do crop models predict phenology, with emphasis on the effect of calibration? Preprint at bioRxiv (2019). Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Response of wheat growth, grain yield and water use to elevated CO 2 under a Free-Air CO 2 Enrichment (FACE) experiment and modelling in a semi-arid environment. Novel multimodel ensemble approach to evaluate the sole effect of elevated CO 2 on winter wheat productivity. Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: a multi-model comparison. Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models. Climate-associated rice yield change in the Northeast China Plain: a simulation analysis based on CMIP5 multi-model ensemble projection.
![apsim source code apsim source code](https://slidetodoc.com/presentation_image/1a066df380d11706a4718c6a06d25d43/image-11.jpg)
Future climate change projects positive impacts on sugarcane productivity in southern China. Future changes in precipitation characteristics in China. 45, 5–32 (2001).Ĭlimate Change in Australia Information for Australia’s Natural Resource Management Regions Technical Report (CSIRO and Bureau of Meteorology, 2015). The effect of bias correction and climate model resolution on wheat simulations forced with a regional climate model ensemble. Effects of different climate downscaling methods on the assessment of climate change impacts on wheat cropping systems. Uncertainty of hydrologic processes caused by bias-corrected CMIP5 climate change projections with alternative historical data sources. Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed. Regionalization and parameterization of a hydrologic model significantly affect the cascade of uncertainty in climate-impact projections. Climate change impact on Mexico wheat production. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Distinguishing variability from uncertainty. Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia. Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins. Designing wheat ideotypes to cope with future changing climate in South-Eastern Australia. Climate-smart management can further improve winter wheat yield in China. Sun, S., Yang, X., Lin, X., Sassenrath, G. How do various maize crop models vary in their responses to climate change factors? Glob. Australian wheat production expected to decrease by the late 21st century. Uncertainty in simulating wheat yields under climate change. Modelling wheat yield change under CO 2 increase, heat and water stress in relation to plant available water capacity in eastern Australia. Adverse weather conditions for European wheat production will become more frequent with climate change. The critical role of extreme heat for maize production in the United States. Our findings highlight the site-specific sources of uncertainty, which should be one step towards understanding uncertainties for more robust climate–crop modelling. This difference is largely due to uncertainty in GCM-projected future rainfall change across locations. The dominant source of uncertainty is GCMs in Australia, whereas in China it is crop models. Generally, the contributions to uncertainty were broadly similar in the two downscaling methods. We partitioned the total uncertainty into sources caused by GCMs, crop models, climate scenarios and the interactions between these three. Here, we simulated rain-fed wheat cropping at four representative locations in China and Australia using eight crop models, 32 global climate models (GCMs) and two climate downscaling methods, to investigate sources of uncertainty in yield response to climate change. An understanding of the major sources of uncertainty in yield change is needed to develop strategies to reduce the total uncertainty. Understanding sources of uncertainty in climate–crop modelling is critical for informing adaptation strategies for cropping systems.