Improvement of a temperature response function for divertor heat flux monitoring in fusion devices

Temperature response functions have been developed to investigate sensor design and divertor heat flux estimation in magnetically confined plasmas. The time-dependent heat flux can be derived by fitting the response function to experimental thermocouple(TC) data. Because the TC signals have a time delay to transit events such as discharge start or confinement transition, the time delay is taken into account in a temperature response function. Such a function accurately describes the signal from each TC channel with time delay in a sensor test using a neutral beam injection. Measurement for commercial TCs shows that the time delay is caused by the finite heat capacity of TC wire and contact heat resistance between TC and target surface.

Spatiotemporal patterns of winter wheat phenology and its climatic drivers based on an improved pDSSAT model

Acquiring spatiotemporal patterns of phenological information and its drivers is essential for understanding the response of crops to climate change and implementing adaptation measures.However,current approaches to obtain phenology and analyse its drivers have deficiencies such as sparse observations,excessive dependence of remote sensing inversion on sensors,and inevitable difficulties in upscaling site-based crop models into larger regions.Based on the Wang-Engel temperature response function,we improved the Crop Estimation through Resource and Environment Synthesis-Wheat(CERES-Wheat)model.First,we calibrated the model at the regional scale and evaluated its performance.Furthermore,the spatiotemporal changes in winter wheat phenology in China from 2000 to 2015 were analysed.The results showed that the improved model significantly enhanced the simulation accuracy of the anthesis and maturity dates by averages of 13%and 12%in most planting areas,especially in the Yunnan-Guizhou Plateau(YG)with improvements of 26%and 28%.The simulated phenology of winter wheat grown in a colder environment(e.g.,the average temperatures during the vegetative growth period range from 0 to 5℃ and from 15 to 20°C,and the reproductive growth period ranges from 10 to 15°C)also notably improved.These results confirmed that the original temperature response function indeed had limitations.Further analyses revealed that the key phenological dates and growth periods over the past 16 years were dominantly advanced and shortened.Specifically,the anthesis date,vegetative growth period(VGP),and reproductive growth period(RGP)indicated obviously spatial characteristics.For example,the anthesis date and VGP in the North China Plain(NCP)and the Middle-Lower Yangtze Plain(YZ)and the RGP in northwestern China(NW)showed opposite trends of delay and prolongation as comparing with the dominant patterns.Sensitivity analysis indicated that the key phenological dates and growth periods were advanced and shortened as the minimum(T_(min))and maximum temperatures(T_(max))rose,while they were postponed and prolonged with the increased precipitation.However,their responses to solar radiation did not show spatial consistency.Additionally,we found that the sensitivity of phenology to climatic factors differed across subregions.In particular,phenology in southwestern China and YG was more sensitive to T_(min),T_(max),and solar radiation than in the NCP and NW.Moreover,the sensitivity to precipitation in NW was higher than that in YZ.Totally,the improved crop model could provide more refined spatial characteristics of phenology at a large scale and benefit to explore its drivers more objectively.Furthermore,our results highlight that different planting areas should adopt suitable adaptation measures to cope with climate change impacts.Ultimately,the improved model is promising to enhance the accuracy of yield prediction and provide powerful tools for assessing regional climate change impact and adaptability.

Uncertainty in Simulating the Impact of Cultivar Improvement on Winter Wheat Phenology in the North China Plain

The phenology model is one of the major tools in evaluating the impact of cultivar improvement on crop pheno-logy. Understanding uncertainty in simulating the impact is an important prerequisite for reliably interpreting the ef-fect of cultivar improvement and climate change on phenology. However, uncertainty induced by different temperat-ure response functions and parameterization methods have not been properly addressed. Based on winter wheat phen-ology observations during 1986-2012 in 47 agro-meteorology observation stations in the North China Plain （NCP）, the uncertainty of the simulated impacts caused by four widely applied temperature response functions and two para- meterization methods were investigated. The functions were firstly calibrated using observed phenology data during 1986-1988 from each station by means of two parameterization methods, and were then used to quantify the impact of cultivar improvement on wheat phenology during 1986-2012. The results showed that all functions and all para-meterization methods could reach acceptable precision （RMSE 〈 3 days for all functions and parameterization meth-ods）, however, substantial differences exist in the simulated impacts between different functions and parameteriza-tion methods. For vegetative growth period, the simulated impact is 0.20 day （10 yr）^-1 [95% confidence interval： -2.81-3.22 day （10 yr）^-1] across the NCP, while for reproductive period, the value is 1.50 day （10 yr）^-1 [-1.03-4.02 day （10 yr）^-1]. Further analysis showed that uncertainty can be induced by both different fimctions and parameteriza-tion methods, while the former has greater influence than the latter. During vegetative period, there is a significant positive linear relationship between ranges of simulated impact and growth period average temperature, while during reproductive period, the relationship is polynomial. This highlights the large inconsistency that exists in most impact quantifying functions and the urgent need to carry out field experiment to provide realistic impacts for all functions. Before applying a simulated effect, we suggest that the function should be calibrated over a wide temperature range.