Extrapolability of hydrological models across diverse geographies and hydrological conditions

Determine the extrapolation ability of process-based hydrological models and data-driven hydrological models under different geographical locations and hydrological conditions, including drought, given the uneven global distribution of surface hydrological observation stations such as runoff gauge stations and eddy-covariance flux towers.

Background

The paper emphasizes that hydrological observation networks are unevenly distributed globally, with dense coverage in developed regions and sparse coverage in remote areas. This limits the ability to validate and quantify how well models generalize to regions and conditions outside their calibration/training domains.

Clarifying extrapolability is central for global applications in water resource management and climate change studies, yet the authors note that this capability is presently unclear, especially under extremes such as drought.