A dynamic agricultural prediction system for large-scale drought assessment on the Sunway TaihuLight supercomputer
| Autor: | Wenyuan Zhang, Shaoqiang Ni, Jiarui Fang, Guorui Huang, Chaoqing Yu, Conrad Zorn, Xiaomeng Huang, Xiao Huang, Jim W. Hall |
|---|---|
| Přispěvatelé: | Water Management |
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Risk analysis Computer science Horticulture computer.software_genre Bayesian inference 01 natural sciences Resource (project management) Accuracy Uncertainty analysis 0105 earth and related environmental sciences Sunway TaihuLight Drought Ensemble forecasting Forestry 04 agricultural and veterinary sciences Supercomputer n/a OA procedure Computer Science Applications 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Spatial variability Data mining Dynamic prediction Scale (map) Agronomy and Crop Science computer |
| Zdroj: | Computers and electronics in agriculture, 154, 400-410. Elsevier |
| ISSN: | 0168-1699 |
| DOI: | 10.1016/j.compag.2018.07.027 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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