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The waterlogging is a serious agrometeorological disaster caused by excessive soil water during crop growth stages. The middle and lower reaches of the Yangtze River are one of the major winter wheat growing regions in China and at the same time they are waterlogging-prone due to their specific climatic conditions. In this study, we integrated a set of tools to analyze the spatiotemporal features of winter wheat waterlogging in this region. We proposed a waterlogging precipitation index (WPI) based on winter wheat yield loss rate and precipitation anomaly percentage and analyzed the frequency, scope, and intensity of winter wheat waterlogging. The results showed that the spring rainfall had a direct and significant effect on winter wheat yield, and the meteorological yield of winter wheat was negatively correlated with precipitation abnormal event from jointing to maturity stages (March to May) across the whole study area. The matching between the waterlogging severity identified by the WPI and historical winter wheat waterlogging records was relatively high. We also discussed the influences of the other nonmeteorological factors, for example, soil texture, topographic and geomorphic conditions, and local disaster-resisting ability, on the extent of waterlogging damage.

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Drought is a recurring extreme climate event over most parts of the world featured by long duration and low predictability. The secular trend of drought is of particular interest for investigators in agriculture, climate change and sustainability domains. In this study, we applied the ensemble empirical mode decomposition (EEMD) method and analyzed the spatio-temporal characteristics of the secular trends of meteorological drought over global land surface during the period 1950–2015 using a self-calibrating Palmer Drought Severity Index (PDSI) product. We found that there were 25.98% PDSI samples had turning point, namely the shift of trend, in the corresponding secular trend series; the probability distribution of the turning points position (period) extracted by EEMD closely follows a normal distribution with mean value at Nov. 1981. We showed that there is large discrepancy in the secular trend types extracted by EEMD and Mann–Kendall test, and exemplified the risk of using a monotonic trend to capture the changes of the intrinsic secular trend of PDSI series. We suggested that there was an accelerated drying trend over global land surface as a whole, but large areas with wetting trend existed in the meantime, especially at the high latitudes in the northern hemisphere. Additionally, we found that the PDSI secular trend change rate exhibits a multidecadal variability of about 50 years or so and it implies a potential relationship with periodic variations of the oceanic and atmospheric current. We showed that the secular trend of PDSI series extracted by EEMD could provide more detailed spatio-temporal characteristics, featured by the shifts of trend and nonlinear property of the secular trend, of global drought than that of the non-parametric or linear regression methods. The secular trend of PDSI could present more insights about the transition and progress of wetting/drying trend over global land surface at multidecadal scale.

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Waterlogging is a serious agro-meteorological disaster caused by excessive soil water, which usually causes tremendous crop yield losses. The region of middle and lower reaches of Yangtze River in China is an important production base of winter wheat, and is an area prone to waterlogging. The risk assessment of winter wheat waterlogging can provide more thorough understanding about the risk-prone environment related with food safety in this region. This study combined a variety of environmental and agricultural factors and assessed the waterlogging risk of winter wheat from the aspects of sensitivity of hazard formative environments, hazard risk, and vulnerability of hazard-affected body using multi-source data. Furthermore, it constructed a compound waterlogging risk assessment model to classify the study area into high, relatively high, moderate, and low risky areas, respectively. The results showed that the proposed model could more comprehensively reflect the occurrence mechanism of winter wheat waterlogging by synchronizing geographical, agricultural, and meteorological factors. The waterlogging regionalization based on the model could reasonably represent the spatial distribution and differentiate regional characteristics of winter wheat waterlogging in the study area. et al. Waterlogging risk assessment for winter wheat using multi-source data in the middle and lower reaches of Yangtze River. Int J Agric & Biol Eng, 2018; 11(5): 198-205.

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