A dynamic agricultural prediction system for large-scale drought assessment on the Sunway TaihuLight supercomputer

•Further acceleration of crop models and high-performance computing for large-scale crop modeling.•Combination of Bayesian inference and Bayesian model average to improve predictive accuracy.•Real-time simulation and prediction based on observational and scenario forces.•Risk analysis of yield losse...

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Vydáno v:Computers and electronics in agriculture Ročník 154; s. 400 - 410
Hlavní autoři: Huang, Xiao, Yu, Chaoqing, Fang, Jiarui, Huang, Guorui, Ni, Shaoqiang, Hall, Jim, Zorn, Conrad, Huang, Xiaomeng, Zhang, Wenyuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.11.2018
Elsevier BV
Témata:
Accuracy
Agricultural management
Agricultural production
Atmospheric models
Bayesian analysis
Bayesian theory
China
Computer simulation
computers
corn
Crop growth
crop models
crop yield
denitrification
Drought
Dynamic prediction
Impact prediction
Optimization
Parallel processing
Parameters
prediction
Real time
Risk analysis
Soil water
Statistical inference
Supercomputer
Supercomputers
uncertainty
Uncertainty analysis
Zea mays
China
Supercomputer
Accuracy
Drought
Risk analysis
Dynamic prediction
ISSN:0168-1699, 1872-7107
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Shrnutí:•Further acceleration of crop models and high-performance computing for large-scale crop modeling.•Combination of Bayesian inference and Bayesian model average to improve predictive accuracy.•Real-time simulation and prediction based on observational and scenario forces.•Risk analysis of yield losses in multiple scales and its spatial dependency. Crop models are widely used to evaluate the response of crop growth to drought. However, over large geographic regions, the most advanced models are often restricted by available computing resource. This limits capacity to undertake uncertainty analysis and prohibits the use of models in real-time ensemble forecasting systems. This study addresses these concerns by presenting an integrated system for the dynamic prediction and assessment of agricultural yield using the top-ranked Sunway TaihuLight supercomputer platform. This system enables parallelization and acceleration for the existing AquaCrop, DNDC (DeNitrification and DeComposition) and SWAP (Soil Water Atmosphere Plant) models, thus facilitating multi-model ensemble and parameter optimization and subsequent drought risk analysis in multiple regions and at multiple scales. The high computing capability also opens up the possibility of real-time simulation during droughts, providing the basis for more effective drought management. Initial testing with varying core group numbers shows that computation time can be reduced by between 2.6 and 3.6 times. Based on the powerful computing capacity, a county-level model parameter optimization (2043 counties for 1996–2007) by Bayesian inference and multi-model ensemble using BMA (Bayesian Model Average) method were performed, demonstrating the enhancements in predictive accuracy that can be achieved. An application of this system is presented predicting the impacts of the drought of May–July 2017 on maize yield in North and Northeast China. The spatial variability in yield losses is presented demonstrating new capability to provide high resolution information with associated uncertainty estimates.
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ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2018.07.027