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Maize is susceptible to drought and low temperatures. In recent years, drought, low temperatures, and their composite events have often occurred during the growth period of maize, causing a huge impact. In this study, maize in the Songliao Plain was used as the research object. Based on the crop water surplus deficit index (CWSDI) and heat index (F(T)), combined with the Theil–Sen median and Mann–Kendall tests (Sen–MK) and empirical orthogonal function (EOF), the temporal trends and spatial patterns of drought and low temperatures in different growth periods of maize were obtained. Copula modelling was used to analyse the joint probability distribution and return period of drought and low temperatures. The results showed that the temporal variation trend of the CWSDI was significantly different at different growth periods of maize, and there were different trends and spatial modal distributions. However, the temporal variation trend of F(T) was similar, showing a downward trend over the entire region. There was no obvious asymmetry or skew‐dependent structure in the distribution of concurrent events of drought and low temperature; however, the joint probability contours of different growth periods were biased to the upper left. The recurrence period of drought and low‐temperature concurrent events was within 2 years, and the recurrence period was basically between 2 and 5 years, with a reduction in drought or low temperatures. [ABSTRACT FROM AUTHOR]

The Songliao Plain is the largest maize (Zea mays L.) cropland area in China and, thus, is most influenced by water stress. To mitigate the adverse impact of water stress on maize yield and quality, various agricultural irrigation strategies have been implemented. Based on land surface temperature and an enhanced vegetation index, this study constructed the temperature vegetation dryness index (TVDI) and combined the Hurst index and Sen trend to analyze the spatiotemporal characteristics of drought and waterlogging. From the correlation between TVDI and gross primary productivity, the weight coefficients of different growth cycles of maize were derived to determine the drought and waterlogging stresses on maize in Songliao Plain for 2000–2020. The drought hazard on the western side of Songliao Plain was high in the west and low in the east, whereas the waterlogging hazard was high in the east. Waterlogging likely persisted according to the spatiotemporal trends and patterns of drought and waterlogging. During the second growth cycle, maize was most severely affected by water stress. There was a spatial heterogeneity in the severity of the hazards and the stress degree of maize. For the reason that precipitation in the study area was concentrated between mid-late July and early August, maize was susceptible to drought stress during the first two growth stages. Irrigation concentrated in the early and middle stages of maize growth and development in the western part of the Songliao Plain reduced the drought stress-induced damage. Spatiotemporally-detected drought and waterlogging couplings and hazards for maize in the Songliao Plain for 2000–2020 provide actionable insights into the prevention and mitigation of such disasters and the implementation of water-saving irrigation practices at the regional scale. [ABSTRACT FROM AUTHOR]

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. • Cloud model qualitative and quantitative mapping of drought and heat events. • Copula function was used to describe the randomness of concurrent extreme events. • Vegetative growth periods (VGP) and reproductive growth periods (RGP) were modelled. [ABSTRACT FROM AUTHOR]