Dynamic agricultural drought risk assessment for maize using weather generator and APSIM crop models

Drought risk assessment provides a vital basis for drought relief and prevention. We developed a dynamic agricultural drought risk (DADR) assessment model to predict drought trends and their impacts on crop yield in real time. A weather generator was employed to produce daily meteorological scenarios to simulate drought trends stochastically. Then, it was used to drive a crop model for simulating drought-induced yield loss. The yield loss rate was calculated to assess the DADR, whereas the cumulative yield loss rate was calculated to measure the cumulative impacts of drought on yield. The drought that occurred in the Liaoning Province in 2000 was selected as a case study, and the DADR was assessed weekly during the maize growth period. The statistical parameters of historical meteorological data were used to prove the rationality of meteorological scenarios. The crop data from 1996 to 2012 were used for crop model calibration and verification. The results showed that, on July 3, 2000, the majority of the Liaoning Province experienced severe or moderate DADR, which showed an increasing trend from east to west, while the highest DADR (over 35%) was noted in Fuxin and Chaoyang. The drought during the maize growth period in 2000 caused an average cumulative yield loss rate of 62.4%. The drought in the early seeding and milk maturity stages had a negligible impact on maize yield, contrary to that in the jointing to tasseling period. Our study provides insights into the implementation of drought relief measures and the development of drought monitoring systems.