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The Carnian Pluvial Event (CPE) induced an intense carbon-cycle disturbance and biological crisis at a global scale. The eruption of the Wrangellia Large Igneous Province (W-LIP) is considered a possible triggering mechanism; however, this cannot fully explain the significant differences in environmental perturbations across different regions. In this paper, we present detailed records of petrography, mercury geochemistry, carbon and sulfur isotopes, trace elements, and provenance data for the Triassic lacustrine facies of the Ordos Basin in the North China Plate, which provide insights into the important effects of volcanic activities, other than the W-LIP, on palaeoenvironment during the CPE interval. Massive emissions of SO 2 , CO 2 , and pyroclastic deposits resulting from these volcanic activities triggered four humid episodes, each characterized by lake-level rise, negative δ13C org excursion, a transition from mudstones to organic-rich shales facies, as well as algal bloom within a time span of 180 kyrs. The establishment of an age framework in the Ordos Basin enables comparative analyses of environmental evolution during the Carnian age across the North China Plate, the western Tethys domain, and the South China Plate. During the period of global climate perturbation driven by the W-LIP eruption, we conclude that short-term localized volcanic activities may have further induced high frequentcy environmental perturbations, which influenced biological shifts and increase organic carbon burial. These short-term volcanic activities with different occurrence times and frequencies complicate local-scale palaeoenvironmental evolutions, which may be an important reason contributing to significant differences in environmental perturbations during the CPE interval across different regions of the globe. • Four humid episodes and sedimentary changes occurred within 0.18Ma in the Ordos Basin during Carnian age. • Environmental changes in the Ordos Basin was related to the local volcanisms in its nearby Qinling orogenic belts. • Short-term local volcanisms can complicate the environmental evolutions in different parts of globe during the CPE interval. • Local volcanic processes may cause inconsistent environment perturbation in different parts of globe during the CPE interval. [ABSTRACT FROM AUTHOR]

There is increasing interest in the application of digital soil mapping (DSM) projections to infer changes in soil carbon across both space and time. This approach relies on the assumption that the spatially modeled soil carbon-environment relationship can be transferred over time. However, this assumption is rarely tested due to a lack of temporally independent validation data. This paper assesses this assumption by developing models of topsoil organic carbon stocks (SOCS) with a deep learning algorithm and data covering mainland China pertaining to the 1980s and 2010s. The temporal prediction performance of models capturing a specific period was assessed by evaluating their performance in the prediction of data during another period. The results revealed that the prediction accuracy of temporal modeling decreased, as indicated by the coefficient of determination, and was lower than that of spatial modeling. The lower prediction accuracy obtained with the DSM-projection approach may result from differences in the magnitudes of influential variables across periods. We found that different levels of environmental similarity and model projection sensitivity to dynamic variables may cause discrepancies in forecast and hindcast accuracy. Our results demonstrate that the prediction error in temporal modeling is related to the degree of environmental similarity between periods. Our findings generally support the implementation of the DSM-projection approach in soil carbon change modeling. However, caution should be exercised, as there exists much uncertainty regarding the projection of spatial models over time. • DSM projection may facilitate a more precise estimation of large-scale soil carbon changes. • Discrepancies in the environment between periods reduce the temporal predictive skill. • The variability in the model performance is related to the magnitude of environmental influences. [ABSTRACT FROM AUTHOR]