08.10.2026 11:30 – 13:00
Margarete-Bieber-Saal (MBS)
Moderator: Lars Ribbe & Mukand Babel

Decoding Resource Interdependencies: From Data to Robust Decisions in the WEFE Nexus

Understanding and managing the complex interdependencies between water, energy, food, land, and ecosystems (the WEFE Nexus) is one of the greatest challenges of our time. As climate extremes intensify and human demands grow, how can we leverage cutting-edge data and modeling to move from observation to evidence-based action?

This session explores the quantitative and analytical tools necessary to decode these interconnected resource systems. We invite participants on a global journey—spanning the Atlantic Forest, the Andes, the Himalayas, and the Middle East—to demonstrate how robust quantification supports strategic planning under deep uncertainty.

BlockIDSpeakerTitle
149Santiago
Penedo
Monitoring and Modelling the Impacts of Different Land Uses on Fluvial Water Quality
Focusing on Nitrogen in the Mata Atlântica: The Guapi-Macacu River Basin
391Maria de Lourdes
Florencio dos Santos
Hydroclimatic Drivers of Agricultural Water Demand in a Brazilian Semiarid Region:
Insights from Water Footprint Assessment
290Adriana Priscila
Sumba Naula
Water Consumption Patterns and Their Drivers Across the Urban–Rural Continuum
in the Ecuadorian Andes: A Machine Learning Approach
429Archana
Sathyan
Linking Surface Energy fluxes to Flash Drought and Terrestrial Water Storage Dynamics
in the Humid Tropics
2139Qais
Daraghma
Quantifying Groundwater-Vegetation Interdependencies under Drought Stress:
An Integrated VHI-SWAT-SPI Approach in the Faria Catchment, Palestine
169Diwakar
Adhikari
Hydroclimatic Changes in the Marsyangdi River Basin, Central Himalaya under CMIP6 Scenarios:
Implications for the Water–Energy–Food–Ecosystem Nexus
336Arash
Malekian
Quantifying Water–Land–Climate Interdependencies to Anticipate Policy Rebound:
Coupled Scenario Modelling for Water Security in a Water Stressed Basin
461Ella
Steins
Robust decision-making for emergency water supply planning
under parametric, structural, and deep uncertainty