A known challenge in hydrological science is the robust uncertainty analysis and statistical representation of surface and underground processes across different scales and environments. Diverse last-generation datasets, encompassing Earth Observation data cubes, ground sensors, laboratory measurements, and physical model outputs, depict with unprecedented realism the spatio-temporal heterogeneity, showing high-order coherence and uncertainty. Stochastic processes and statistical analysis can be used to capture complex hydrological dynamics and inform decision making for water and landscape managing, natural hazard assessment, hydroclimate mitigation measures, and hydrological engineering.
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.).
Lionel Benoit