Detection of major weather patterns reduces number of simulations in climate impact studies

Ababaei, Behnam, and Najeeb, Ullah (2020) Detection of major weather patterns reduces number of simulations in climate impact studies. Journal of Agronomy and Crop Science, 206 (3). pp. 376-389.

[img] PDF (Pubished Version) - Published Version
Restricted to Repository staff only

View at Publisher Website:


With climate change posing a serious threat to food security, there has been an in- creased interest in simulating its impact on cropping systems. Crop models are useful tools to evaluate strategies for adaptation to future climate; however, the simulation process may be infeasible when dealing with a large number of G × E × M combina- tions. We proposed that the number of simulations could significantly be reduced by clustering weather data and detecting major weather patterns. Using 5, 10 and 15 clusters (i.e., years representative of each weather pattern), we simulated phenology, cumulative transpiration, heat-shock-induced yield loss (heat loss) and grain yield of four Australian cultivars across the Australian wheatbelt over a 30-year period under current and future climates. A strong correlation (r2≈1) between the proposed method and the benchmark (i.e., simulation of all 30 years without clustering) for phenology suggested that average duration of crop growth phases could be predicted with sub- stantially fewer simulations as accurately as when simulating all 30 seasons. With mean absolute error of 0.64 days for phenology when only five clusters were identi- fied, this method had a deviation considerably lower than the reported deviations of calibrated crop models. Although the proposed method showed higher deviations for traits highly sensitive to temporal climatic variability such as cumulative transpi- ration, heat loss and grain yield when five clusters were used, significantly strong correlations were achieved when 10 or 15 clusters were identified. Furthermore, this method was highly accurate in reproducing site-level impact of climate change. Less than 7% of site × general circulation model (GCM) combinations (zero for phenology) showed incorrect predication of the direction (+/−) of climate change impact when only five clusters were identified while the accuracy further increased at the regional level and with more clusters. The proposed method proved promising in predicting selected traits of wheat crops and can reduce number of simulations required to predict crop responses to climate/management scenarios in model-aided ideotyping and climate impact studies.

Item ID: 69767
Item Type: Article (Research - C1)
ISSN: 1439-037X
Keywords: climate change, clustering, crop modelling, ideotyping, phenology, weather patterns
Copyright Information: © 2020 Blackwell Verlag GmbH
Date Deposited: 26 Oct 2021 01:43
FoR Codes: 37 EARTH SCIENCES > 3702 Climate change science > 370299 Climate change science not elsewhere classified @ 30%
41 ENVIRONMENTAL SCIENCES > 4101 Climate change impacts and adaptation > 410199 Climate change impacts and adaptation not elsewhere classified @ 30%
49 MATHEMATICAL SCIENCES > 4905 Statistics > 490501 Applied statistics @ 40%
SEO Codes: 19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1901 Adaptation to climate change > 190101 Climate change adaptation measures (excl. ecosystem) @ 50%
26 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 2699 Other plant production and plant primary products > 269901 Climate adaptive plants @ 50%
Downloads: Total: 1
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page