A known challenge in hydrological science is the robust uncertainty analysis of physical processes through analysis of records from regional and global scale ground, coastal and marine observations (on point basis or gridded), satellite and reanalysis data, remote-sensed records, laboratory measurements and computational outputs. The stochastic analysis of analogies among hydroclimatic and hydrodynamic processes in a vast range of scales offers insights on coherence and uncertainty (marginal and dependence structures, intermittent and fractal behaviour, trends, irreversibility, etc.). Stochastic approaches can also serve as information for water-related management purposes, natural hazard assessment, and mitigation measures, e.g. in terms of hydrologic design estimation.
This session welcomes, but is not limited to, contributions on stochastic spatio-temporal analysis, modelling, simulation and prediction of hydrological-cycle and hydrodynamic processes (streamflow, precipitation, temperature, evapotranspiration, humidity, dew-point, soil moisture, groundwater, etc.), water-energy-food nexus processes (agricultural, financial and other related fields, solar radiation, wind speed, reservoir stage, etc.), laboratory measurements (i.e., small-scale models for large-scale applications), and computational outputs (e.g., concerning floods, droughts, climatic models, etc.).