Large-sample hydrology (LSH) relies on data from large sets (tens to thousands) of
catchments to go beyond individual case studies and derive robust conclusions on …

Continental-to global-scale hydrologic and land surface models increasingly include
representations of the groundwater system. Such large-scale models are essential for …

Droughts that exceed the magnitudes of historical variation ranges could occur increasingly
frequently under future climate conditions. However, the time of the emergence of …

Globally, flood risk is projected to increase in the future due to climate change and
population growth. Here, we quantify the role of dams in flood mitigation, previously …

WaterGAP is a global hydrological model that quantifies human use of groundwater and
surface water as well as water flows and water storage and thus water resources on all land …

Droughts are among the costliest natural disasters. They affect wide regions and large
numbers of people worldwide by tampering with water availability and agricultural …

Inferring the mechanisms causing river flooding is key to understanding past, present, and
future flood risk. However, a quantitative spatially distributed overview of the mechanisms …

Global impact models represent process-level understanding of how natural and human
systems may be affected by climate change. Their projections are used in integrated …

As a genre of physics-informed machine learning, differentiable process-based hydrologic
models (abbreviated as δ or delta models) with regionalized deep-network-based …

Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of
sectors including water availability, food security, and energy production. Given continuing …