Mon. May 26, 2025 5:15 PM - 7:15 PM Poster Hall (Exhibition Hall 7&8, Makuhari Messe)

convener:Abhishek Abhishek(Indian Institute of Technology Roorkee), WENPENG ZHAO(Yangzhou University), Brijesh Kumar Yadav(Indian Institute of Technology Roorkee), Tsuyoshi Kinouchi(Institute of Science Tokyo)

Confounding impacts of anthropogenic interventions and natural climate variability have altered the mean state, variance, and tradeoff in the terrestrial hydrological cycle and its constituent water storage and flux components, with continually growing magnitude and uncertainty in the Anthropocene. In the premise of 1) the tendency of studies focusing on one component (of water storage, e.g., surface water, soil moisture, groundwater; or of fluxes, e.g., precipitation, evaporation runoff) as a standalone, and 2) inherent limitations of the traditional methods for mapping these variables, here, we invite studies leveraging a multitude of state-of-the-art methods based on modeling, reanalysis, remote sensing, and machine learning. Synergistic assessment of multiple fluxes/components will be instrumental in the hydrological process understanding and effectively managing water resources for a sustainable society.

The scope of this session is to create an interdisciplinary forum by providing a common platform for researchers from academia, industry, NGOs, and government agencies belonging to wide geographic diversity to discuss recent scientific results related to, but not limited to:

1) Proof-of-principle and proof-of-concept studies dealing with surface water- groundwater interaction.

2) Understanding the impact of global warming and anthropogenic activities (e.g., urbanization, water diversion) on surface water, soil moisture, and groundwater at various spatial (e.g., regional, basin, nation) and temporal (from sub-hourly to decadal) scales.

3) Quantifying trends and variability in dwindling quantity and deteriorating quality of various (sub)surface resources and their management in a warming climate.

4) Improved methods (e.g., artificial intelligence) for assessing past and projected hydrological cycle changes and related hydroclimate extremes (floods, droughts), compound events, and their transition and propagation behaviors.