Research on Vulnerability Curve of Waterlogging Disaster for Maize Based on CERES-Maize Model in the Midwest of Jilin Province
Guo Enliang, Jiquan Zhang, Yongfang Wang, Zhenhua Dong
Available Online November 2016.
- https://doi.org/10.2991/rac-16.2016.106How to use a DOI?
- The vulnerability of curve; CERES-Maize model; waterlogging disaster; Midwest of Jilin Province
- Under the background of global warming, the loss caused by extreme precipitation is becoming more and more serious. As a bridge between risk and disaster losses, at present, research on vulnerability curve has become research focus in the field of disaster risk In this study, based on meteorological data, crop physiological data, and field management data and so on, to select typical waterlogging disaster years as a case, the growth process of Maize in the Midwest of Jilin province was simulated by using the CERES-Maize model of localization and spatial scaling. The disaster intensity index is calculated by copula function, and then builds the construction of waterlogging disaster vulnerability curve of Maize. The results show that: vulnerability curve of waterlogging disaster for maize in descending order are: the emergence - jointing, jointing - heading stage, heading - milk stage, milky - maturity, The results can be used as mitigate water logging disaster for maize growth, choose a reasonable period of irrigation and drainage facilities provide an important basis, in order to achieve the result of optimal Maize planting arrangement.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Guo Enliang AU - Jiquan Zhang AU - Yongfang Wang AU - Zhenhua Dong PY - 2016/11 DA - 2016/11 TI - Research on Vulnerability Curve of Waterlogging Disaster for Maize Based on CERES-Maize Model in the Midwest of Jilin Province BT - 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC-2016) PB - Atlantis Press UR - https://doi.org/10.2991/rac-16.2016.106 DO - https://doi.org/10.2991/rac-16.2016.106 ID - Enliang2016/11 ER -