Traditional hydrological sciences have to a large extent been devoted to case study approaches, using site-specific data and methods. This has resulted in fragmentation and lack of knowledge accumulation over time. However, the contemporary global movement is now to share data and make use of large data samples. Opening up public sector data and information for re-use has a significant and currently untapped potential to act as an engine for innovation in hydrology. The data can be shared in new technical platforms, as virtual observatories or virtual laboratories, which also include protocols and open source tools for collaborative experiments. Such new technical infrastructures for collaboration facilitate multi-basin modelling across large geographical domains to perform comparative hydrological research of diverse physiography. In addition, the data sharing make it possible to explore correlations between results from different research groups world-wide and explore the reproducibility of research experiments.

Large-sample data sets facilitate understanding of ‘hydrological consistency’ through rigorous testing and comparison of competing model hypothesis and structures, robustness of generalizations, classification, regionalization and model transfer. In addition, large or diverse datasets enable process understanding across scales from national/regional, to catchment or hillslope by an integration of top-down and bottom-up modelling approaches. It may also falsify hypothesis, which could not be rejected on a limited dataset. The progresses in open data offers new opportunities to compare, contrast and combine datasets; to put local, experimental data in a larger-scale context; to quantify the information content of data for hydrological analysis; to discover behavior and patterns across regional, national and international scales; and to understand trends and drivers of hydrological processes.

In this session attention should be focused on hydrological research based on sharing or exploiting large data samples and information from many varied open sources. We especially welcome contributions on: (i) Quality assurance and evaluation of public sector data portals; (ii) New concepts for numerical analysis, evaluation and multi-basin modelling across large domains; (iii) Analysis of dominant hydrological control when comparing environments and scales; (iv) Predictions of emergent patterns and change over large geographical domains; (v) Technical platforms for sharing data and numerical methods; (vi) Protocols for and challenges in collaborative experiments; (vii) Lessons learnt from experiments, which did not work out as expected and lead to a substantial revision of the a priori assumptions.