Simulating Nature’s randomness with CoSMoS - A Versatile Stochastic Modeling Framework for Hydrometeorological Phenomena

Nature depends on the inherent unpredictability of randomness, a significant force influencing hydrometeorological processes. While physics provides sophisticated models, understanding the variability within randomness is crucial for evaluating environmental risks. Despite the availability of numerous stochastic models tailored to specific statistical properties, identifying essential features for accurate simulations across time, space, and scales remains a challenge. This presentation outlines the progress in CoSMoS, a user-friendly stochastic modeling framework that advances from basic scenarios to complex multisite and space-time simulations. The underlying philosophy of this framework is to faithfully replicate the probabilities describing the occurrences of magnitudes and correlations in space and time. CoSMoS excels in generating time series for various hydroclimatic variables and simulating intricate space-time phenomena, as demonstrated by its effectiveness in replicating storms, cyclones, and air mass collisions. This showcases its versatility in capturing complex behaviors across different scales. References Papalexiou, S. M., Serinaldi, F., & Clark, M. P. (2023). Large-Domain Multisite Precipitation Generation: Operational Blueprint and Demonstration for 1,000 Sites. Water Resources Research, 59(3), e2022WR034094. https://doi.org/10.1029/2022WR034094 Papalexiou, S. M. (2022). Rainfall Generation Revisited: Introducing CoSMoS-2s and Advancing Copula-Based Intermittent Time Series Modeling. Water Resources Research, 58(6), e2021WR031641. https://doi.org/10.1029/2021WR031641 Papalexiou, S. M., Serinaldi, F., & Porcu, E. (2021). Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond. Water Resources Research, 57(8), e2020WR029466. https://doi.org/10.1029/2020WR029466 Papalexiou, S. M., & Serinaldi, F. (2020). Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity. Water Resources Research, 56(2), e2019WR026331. https://doi.org/10.1029/2019WR026331 Papalexiou, S. M. (2018). Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency. Advances in Water Resources, 115, 234–252. https://doi.org/10.1016/j.advwatres.2018.02.013 Papalexiou, S. M., Markonis, Y., Lombardo, F., AghaKouchak, A., & Foufoula‐Georgiou, E. (2018). Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes. Water Resources Research, 54(10), 7435–7458. https://doi.org/10.1029/2018WR022726