PICO
The rapid growth in the availability and diversity of hydro-meteorological data is transforming our ability to monitor, understand, model, and predict hydrological extremes. Global and continental datasets, including reanalysis, satellite and gridded products, provide unprecedented spatial and temporal coverage, while national, regional and local networks often offer higher-resolution observations of catchment-scale processes. This diversity creates new opportunities but also raises key questions: Which data are best suited to different purposes, how should they be integrated, and how does data choice affect hydrological analyses and modelling outcomes?
This PICO session brings together researchers working with different types and scales of hydro-meteorological data to explore how data choice, quality, spatial and temporal resolution, uncertainty and availability influence hydrological modelling and the assessment of extremes. Rather than focusing primarily on the development of individual precipitation, evapotranspiration or other hydro-meteorological products, or on hazard and risk processes themselves, the session focuses on how the choice, quality and scale of data shape the analysis, modelling and interpretation of hydro-meteorological extremes. We particularly encourage studies comparing global products with national, regional, catchment-scale or local datasets and observational networks.
Contributions may address precipitation, temperature, evapotranspiration, soil moisture, snow, streamflow, groundwater and remote-sensing products, with applications to rainfall-runoff modelling, droughts, floods, compound events, trend and frequency analysis, forecasting and water-resources assessment.
The session also welcomes studies on data quality and gaps, aggregation, bias correction, downscaling, data fusion, ensemble and multi-model approaches, uncertainty propagation and reproducible workflows. Machine learning, artificial intelligence, cloud computing and multi-source data integration are encouraged where they improve the reliability, transferability or operational value of hydrological analyses.
A key objective is to move beyond identifying which dataset performs best towards understanding why different data sources, resolutions and processing choices lead to different hydrological interpretations, model responses and decisions.