For large parts of the world, such as tropical regions and remote areas, detailed, long time hydrological records are not available. Many of the processes occurring in these catchments, such as subsurface flows and soil-vegetation interaction, are poorly characterized. Despite of this scarcity, there is often an urgent need for adequate water resources management and hydrological predictions to help socio-economic development. In such conditions, it is essential to aim data collection at dominant hydrological processes and to extract information with maximum predictive capacity.

This session focuses on methods to deal with data scarcity for water resources management. This ranges from methods to evaluate the predictive capacity of models in view of the available data, to new data collection techniques and the identification of deficiencies in methods aimed at data rich environments.

Although the session is open to contributions from any data scarce region, we would particularly welcome contributions from complex and poorly studied environments such as Patagonia and the African Great Rift Valley.