Wallach, D., & Rivington, M. (2013). Development of a common set of methods and protocols for assessing and communicating uncertainties (Vol. 2).
Abstract: This reports sets out an outline approach to create definitions of uncertainty and how it might be classified. This is not a prescriptive approach rather it should be seen as a starting point from which further development can be made by consensus with CropM partners and across MACSUR Themes. We propose both a numerical quantification of uncertainty and text based classification scheme. The rational is to be able to both establish the terms and definitions in quantifying the impact of uncertainty on model estimates and have a scheme to enable identification of connectivity between types and sources of uncertainty. The aim is to establish a common set of terms and structure within which they operate that can be used to guide work within CropM. No Label
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Wallach, D., Thorburn, P., Asseng, S., Challinor, A. J., Ewert, F., Jones, J. W., et al. (2016). Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions (Vol. 8).
Abstract: Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. Several ways of quantifying prediction uncertainty have been explored in the literature, but there have been no studies of how the different approaches are related to one another, and how they are related to some overall measure of prediction uncertainty. Here we show that all the different approaches can be related to two different viewpoints about the model; either the model is treated as a fixed predictor with some average error, or the model can be treated as a random variable with uncertainty in one or more of model structure, model inputs and model parameters. We discuss the differences, and show how mean squared error of prediction can be estimated in both cases. The results can be used to put uncertainty estimates into a more general framework and to relate different uncertainty estimates to one another and to overall prediction uncertainty. This should lead to a better understanding of crop model prediction uncertainty and the underlying causes of that uncertainty. This study was published as (Wallach et al. 2016)
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Wehrheim, P. (2016). Agriculture and land use in the Commission proposals for the 2030 Climate and Energy Framework (Vol. 9 C6 -).
Abstract: Introduction: policy context•Impact Assessment: options, models, examples•Proposal for Effort Sharing Regulation and LULUCF Regulation•Conclusions and Outlook: more work for modellers 1. Fully in line with Paris Agreement, no backsliding on robustness and transparency2.Provides for continuity•Addresses Member States and not individual farmers or foresters•Stand-alone LULUCF pillar•No-debit rule (from KP)•Flexibility within LULUCF and from ESR to LULUCF3.Proposes limited innovations•Flexibility to the ESR up to 280 mt CO2•Aligning accounting rules (AF,CM/GM)•Defining EU-internal process to set national forest management levels•Simplifying administrationConclusions (2)
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Woolnough, S. (2015). Climate Modelling and Sub-seasonal to Seasonal Prediction: Opportunities and Challenges (Vol. 5).
Abstract: Dr Steve Woolnough is a Principal Research Fellow in the Climate directorate of the National Centre for Atmospheric Science, and leads their Tropical Group. His interests are in the variability of the Tropical Climate System on intraseasonal to seasonal timescales, and the representation of the tropical climate system in weather and climate prediction models. He is a member of three international panels of the WMO including the Steering Group of their sub-seasonal to seasonal prediction project. Dr Woolnough will discuss the current state of climate modelling and introduce some of the uncertainties in prediction of regional climate change, and the opportunities to narrow these uncertainties. He will also discuss the current state of sub-seasonal to seasonal prediction and introduce the WCRP/WWRP Sub-seasonal Prediction Project, a new WMO project to promote research into and application of operational prediction systems. No Label
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Yin, X. (2015). Effects of climatic factors, drought risk and irrigation requirement on maize yield in the northeast farming region of China over 1961 to 2010 (Vol. 5).
Abstract: The Northeast Farming Region (NFR) is the most important and the largest rain-fed maize production region in China, accounting for 30% of China’s maize. We investigated the effects of climatic factors, drought risk and irrigation requirement on maize yield in different maize growth phases during 1961 to 2010 using a statistical analysis of observed yield from 44 stations in NFR. We divided the maize growing season into four growth phases, comprising seeding, vegetative, flowering and maturity. The dual crop coefficient was used to calculate crop evapotranspiration and soil water balance during the maize growing season. The effects of mean temperature, radiation, effective rainfall, water deficit, drought stress days, actual crop evapotranspiration (ETa) and irrigation requirement in different growth phases were included in the statistical model to predict maize yield. During the period 1961 to 2010, mean temperature increased significantly in all growth phases in NFR, while radiation decreased significantly in southern NFR in the seeding, vegetative and flowering phases. Effective rainfall increased in the seeding and vegetative phases leading to less water deficit, whereas decreased effective rainfall in the flowering and maturity phases enhanced water deficit. More days with drought stress were concentrated in western NFR where larger volumes of irrigation were needed. Our results indicate that the increase of mean temperature in the seeding and maturity phases was beneficial for maize yield, higher ETa in each growth phase would lead to yield increase, but too high rainfall would damage maize yield. The results also show that water deficit and drought stress days had significant negative effects on maize yield, and the absence of irrigation would manifest such effects on maize production in NFR. Therefore, the development of irrigation and drainage systems is highly needed for ensuring the stability of maize production in NFR. In addition, other adaptation measures like introducing new cultivars and optimizing soil and crop management to better conserve soil water would be beneficial for future maize production. No Label
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