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Convergence at the Horizon towards Predictive Resilience in a Non-Stationary World
DOI:
https://doi.org/10.30564/jasr.v9i1.13122Abstract
Climate change and technological advancement are driving a paradigm shift in the atmospheric and hydrometeorological sciences. This transformation is reflected in the complex interactions between ocean and atmospheric dynamics, the role of climate phenomena such as El Niño and La Niña, and the consequent effects on global water resources. This editorial argues that the critical task for the coming decade is to advance from hazard-focused forecasting toward the robust prediction of systemic impacts and societal resilience. We highlight three convergent frontiers of innovation: the integration of artificial intelligence and machine learning across the observational-modeling-prediction chain; the explicit coupling of human and water-energy-food systems within Earth system models; and the development of a “digital twin” framework for the Earth system. Success will require transcending disciplinary boundaries, promoting open science and data democratization, and fostering a new “convergence science” that interweaves physical dynamics, data science, socio-economics, and governance. The ultimate aim is to deliver actionable intelligence for anticipatory adaptation and enhanced climate resilience.
Keywords:
AI/ML-Earth System Integration; Socio-Hydrometeorological Coupling; Predictive Resilience; Digital Twin; Convergence Science; Non-Stationarity; Climate AdaptationReferences
[1] Sun, S., Zhang, Q., Shi, C.X., et al., 2024. Urban irrigation reduces moist heat stress in Beijing, China. npj Climate and Atmospheric Science. 7, 36. DOI: https://doi.org/10.1038/s41612-024-00585-6
[2] Camps-Valls, G., Fernández-Torres, M.-Á., Cohrs, K.-H., et al., 2025. Artificial Intelligence for Modeling and Understanding Extreme Weather and Climate Events. Nature Communications. 16, 1919.
[3] Bauer, P., Dueben, P.D., Hoefler, T., et al., 2021. The Digital Revolution of Earth-System Science. Nature Computational Science. 1(2), 104–113.
[4] Clark, M.P., Bierkens, M.F.P., Samaniego, L., et al., 2017. The Evolution of Process-Based Hydrologic Models: Historical Challenges and the Collective Quest for Physical Realism. Hydrology and Earth System Sciences. 21(7), 3427–3440.
[5] Gleeson, T., Befus, K.M., Jasechko, S., et al., 2016. The Global Volume and Distribution of Modern Groundwater. Nature Geoscience. 9, 161–167.
[6] Holloway, A., Triyanti, A., Rafliana, I., et al., 2019. Leave No Field Behind: Future-Ready Skills for a Risky World. Progress in Disaster Science. 1, 100002.
[7] Milly, P.C., Betancourt, J., Falkenmark, M., et al., 2008. Stationarity Is Dead: Whither Water Management? Science. 319(5863), 573–574. DOI: https://doi.org/10.1126/science.1151915
[8] Zhang, Q., Yu, H.Q., Li, J.F., et al., 2023. Divergent effectiveness of irrigation in enhancing food security in droughts under future climates with various emission scenarios. npj Climate and Atmospheric Science. 6, 40. DOI: https://doi.org/https://doi.org/10.1038/s41612-023-00362-x
[9] Tang, S.L., Zhang, Q., Gong, X.T., et al., 2026. Emergent constraints reveal underprediction of future global water availability under anthropogenic forcing. Global and Planetary Change. 257, 105252.
[10] Reichstein, M., Camps-Valls, G., Stevens, B., et al., 2019. Deep Learning and Process Understanding for Data-Driven Earth System Science. Nature. 566(7743), 195–204.
[11] Shepherd, T.G., Boyd, E., Calel, R.A., et al., 2018. Storylines: An Alternative Approach to Representing Uncertainty in Physical Aspects of Climate Change. Climatic Change. 151, 555–571.
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Copyright © 2026 Qiang Zhang

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Qiang Zhang