Quantifying Multi-hazards and Impacts Over Different Growth Periods of Maize: A Study Based on Index Construction
Dan Chen , Ying Guo , Rui Wang , Yunmeng Zhao , Kaiwei Li , Jiquan Zhang , Xingpeng Liu , Zhijun Tong , Chunli Zhao
International Journal of Disaster Risk Science ›› 2023, Vol. 14 ›› Issue (5) : 822 -839.
Quantifying Multi-hazards and Impacts Over Different Growth Periods of Maize: A Study Based on Index Construction
Owing to the complexity and variability of global climate, the study of extreme events to ensure food security is particularly critical. The standardized precipitation requirement index (SPRI) and chilling injury index (ICi) were introduced using data from agrometeorological stations on the Songliao Plain between 1981 and 2020 to identify the spatial and temporal variability of drought, waterlogging, and low-temperature cold damage during various maize growth periods. Compound drought and low-temperature cold damage events (CDLEs) and compound waterlogging and low-temperature cold damage events (CWLEs) were then identified. To measure the intensity of compound events, the compound drought and low-temperature cold damage magnitude index (CDLMI), and compound waterlogging and low-temperature cold damage magnitude index (CWLMI) were constructed by fitting marginal distributions. Finally, the effects of extreme events of various intensities on maize output were examined. The findings demonstrate that: (1) There were significant differences in the temporal trends of the SPRI and ICi during different maize growth periods. Drought predominated in the middle growth period (MP), waterlogging predominated in the early growth period (EP) and late growth period (LP), and both drought and waterlogging tended to increase in intensity and frequency. The frequency of low-temperature cold damage showed a decreasing trend in all periods. (2) The CDLMI and CWLMI can effectively determine the intensity of CDLEs and CWLEs in the study area; these CDLEs and CWLEs had higher intensity and frequency in the late growth period. (3) Compared to single events, maize relative meteorological yield had a more significant negative correlation with the CDLMI and CWLMI.
Compound events magnitude index / Drought / Growth periods / Low-temperature cold damage / Spatiotemporal distribution / Waterlogging
| [1] |
|
| [2] |
Bi, W., M. Li, B. Weng, D. Yan, Z. Dong, J. Feng, and H. Wang. 2023. Drought-flood abrupt alteration events over China. Science of The Total Environment 875: Article 162529. |
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
Dang, Y., L. Qin, L. Huang, J. Wang, B. Li, and H. He. 2022. Water footprint of rain-fed maize in different growth stages and associated climatic driving forces in Northeast China. Agricultural Water Management 263: Article 107463. |
| [8] |
De Luca, P., and M.G. Donat. 2023. Projected changes in hot, dry, and compound hot-dry extremes over global land regions. Geophysical Research Letters 50(13): Article e2022GL102493. |
| [9] |
Dhaliwal, D.S., and M.M. Williams. 2022. Evidence of sweet corn yield losses from rising temperatures. Scientific Reports 12(1): Article 18218. |
| [10] |
Eck, M.A., A.R. Murray, A.R. Ward, and C.E. Konrad. 2020. Influence of growing season temperature and precipitation anomalies on crop yield in the southeastern United States. Agricultural and Forest Meteorology 291: Article 108053. |
| [11] |
|
| [12] |
Feng, S., Z. Hao, X. Zhang, and F. Hao. 2021. Changes in climate-crop yield relationships affect risks of crop yield reduction. Agricultural and Forest Meteorology 304–305: Article 108401. |
| [13] |
|
| [14] |
Guan, X., Y. Zang, Y. Meng, Y. Liu, H. Lv, and D. Yan. 2021. Study on spatiotemporal distribution characteristics of flood and drought disaster impacts on agriculture in China. International Journal of Disaster Risk Reduction 64: Article 102504. |
| [15] |
Guo, Y., J. Zhang, K. Li, H. Aru, Z. Feng, X. Liu, and Z. Tong. 2023. Quantifying hazard of drought and heat compound extreme events during maize (Zea mays L.) growing season using Magnitude Index and Copula. Weather and Climate Extremes 40: Article 100566. |
| [16] |
|
| [17] |
Hu, J., W. Yu, P. Liu, B. Zhao, J. Zhang, and B. Ren. 2023. Responses of canopy functionality, crop growth and grain yield of summer maize to shading, waterlogging, and their combination stress at different crop stages. European Journal of Agronomy 144: Article 126761. |
| [18] |
Huang, C., W. Zhang, H. Wang, Y. Gao, S. Ma, A. Qin, Z. Liu, et al. 2022. Effects of waterlogging at different stages on growth and ear quality of waxy maize. Agricultural Water Management 266: Article 107603. |
| [19] |
Kim, G.-U., H. Oh, Y.S. Kim, J.-H. Son, and J.-Y. Jeong. 2023. Causes for an extreme cold condition over Northeast Asia during April 2020. Scientific Reports 13(1): Article 3315. |
| [20] |
|
| [21] |
|
| [22] |
Li, Z., Z. Zhang, J. Zhang, Y. Luo, and L. Zhang. 2021. A new framework to quantify maize production risk from chilling injury in Northeast China. Climate Risk Management 32: Article 100299. |
| [23] |
Ling, M., X. Guo, X. Shi, and H. Han. 2022. Temporal and spatial evolution of drought in Haihe River Basin from 1960 to 2020. Ecological Indicators 138: Article 108809. |
| [24] |
Liu, W., Z. Li, Y. Li, T. Ye, S. Chen, and Y. Liu. 2022. Heterogeneous impacts of excessive wetness on maize yields in China: Evidence from statistical yields and process-based crop models. Agricultural and Forest Meteorology 327: Article 109205. |
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
Shi, J., L. Cui, and Z. Tian. 2020. Spatial and temporal distribution and trend in flood and drought disasters in East China. Environmental Research 185: Article 109406. |
| [31] |
|
| [32] |
Sun, J., T. Liu, S. Xie, J. Xiao, L. Huang, Z. Wan, and K. Zhong. 2023. Will extreme temperature events emerge earlier under global warming? Atmospheric Research 288: Article 106745. |
| [33] |
|
| [34] |
Wang, T., N. Li, Y. Li, H. Lin, N. Yao, X. Chen, D. Li Liu, Q. Yu, and H. Feng. 2022. Impact of climate variability on grain yields of spring and summer maize. Computers and Electronics in Agriculture 199: Article 107101. |
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
Yang, Y., K. Li, S. Wei, S. Guga, J. Zhang, and C. Wang. 2022. Spatial-temporal distribution characteristics and hazard assessment of millet drought disaster in Northern China under climate change. Agricultural Water Management 272(C): Article 107849. |
| [39] |
Yang, W., L. Zhang, and Y. Gao. 2023. Drought and flood risk assessment for rainfed agriculture based on Copula-Bayesian conditional probabilities. Ecological Indicators 146: Article 109812. |
| [40] |
Yu, E., D. Liu, J. Yang, J. Sun, L. Yu, and M.P. King. 2023. Future climate change for major agricultural zones in China as projected by CORDEX-EA-II, CMIP5 and CMIP6 ensembles. Atmospheric Research 288: Article 106731. |
| [41] |
Zhang, Y., Z. Hao, S. Feng, X. Zhang, and F. Hao. 2022. Changes and driving factors of compound agricultural droughts and hot events in eastern China. Agricultural Water Management 263: Article 107485. |
| [42] |
|
| [43] |
|
| [44] |
|
| [45] |
|
| [46] |
|
| [47] |
Zhou, Y., P. Zhou, J. Jin, C. Wu, Y. Cui, Y. Zhang, and F. Tong. 2022. Drought identification based on Palmer drought severity index and return period analysis of drought characteristics in Huaibei Plain China. Environmental Research 212: Article 113163. |
| [48] |
|
/
| 〈 |
|
〉 |