Joint analysis of drought and heat events during maize (Zea mays L.) growth periods using copula and cloud models: A case study of Songliao Plain

Due to global warming, it is necessary to study the influence of extreme climate and concurrent events on crop growth. The study area was the Songliao Plain, where drought events frequently occur. First, the daily meteorological data of 14 meteorological stations from 1981 to 2016 were collected to analyze the temporal and spatial changes in the crop water surplus and deficit index, extreme growing degree-days, and heat stress intensity during different growth stages of maize. Second, the cloud model was used to describe the fuzziness of concurrent events (simultaneous drought and heat), and mutual mapping between qualitative and quantitative data was undertaken. The fuzzy certainty degree of the influence of different degrees of concurrent events on maize was calculated. Third, the copula function was used to describe the randomness of concurrent extreme events and calculate the joint probability distribution and return period. An assessment method was proposed for concurrent events from the perspective of system uncertainty. Finally, we analyzed the relationship between concurrent events and maize yield, which showed different degrees of water deficit and warming trends during each growth period. Crops were most affected by extreme weather during the reproductive growth period (RGP). During the vegetative growth period (VGP), the temperature increase was higher than in other periods, especially in the high-latitude region. The frequency of mild concurrent events was higher during the VGP and RGP. During the vegetative and reproductive period, the average occurrence probability of mild, moderate, and severe concurrent events was 21.9%, 1.7%, and 0.35%, respectively, whereas during the RGP, it was 23.1%, 8.2%, and 0.12%, respectively. The present study provides a meaningful reference for a better understanding of the occurrence laws of drought, heat, and concurrent events during crop growth periods and how to optimize the agricultural management of maize.