The complex and nonlinear characteristics of atmospheric dynamics, along with their high dimensionality and interconnections across several spatial and temporal factors, pose significant challenges in comprehending, modeling, and predicting hydroclimatic extreme events. Understanding complex systems such as the atmosphere requires establishing causal relationships; therefore, this study focuses on hydroclimatic extremes, including heat waves, droughts, and extreme precipitation, which continue to be emphasized in climate change projections. Hydroclimatic extreme events pose a significant threat to agricultural sustainability, impacting crop yields, public health, migration patterns and ecological balance. The present research delves into the spatial and temporal dynamics of extreme precipitation, droughts, and heatwaves, specifically analyzing their influence on corn and soybean yields. Moreover, we aim to discover the causal relationships between these extreme events and crop yields by employing cross convergent mapping (CCM). CCM is a technique that relies on nonlinear state space reconstruction and is capable of distinguishing between causality and correlation. In accordance with Takens’ embedding theory, the attractor is reconstructed by CCM using time series data. This reconstruction allows for the identification and measurement of causation through cross-mapping prediction. The study uses the Evaporative Demand Drought Index (EDDI) as a metric of agricultural drought, the Palmer Drought Severity Index (PDSI) as an indicator of hydrological drought, together with maximum air temperature, extreme precipitation, and data on corn and soybean yields. Beyond that, this investigation is carried out on rainfed agricultural lands in Iowa with the specific aim of elucidating the impacts of hydroclimatic extreme events. Agricultural sustainability research and the quantification of economic consequences and impacts of extreme events on agriculture will benefit significantly from the findings of this investigation.
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Baydaroğlu et al. (2024) studied this question.
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