Transferability of data-driven models trained in data-rich regions to unknown domains

Ascertain the transferability of data-driven hydrological models trained in data-rich regions to geographically and climatologically unknown target regions when the target region’s climate, hydrology, and environmental characteristics are absent from the training dataset.

Background

While data-driven models can perform well in data-rich scenarios, the paper highlights uncertainty about their ability to generalize to locations with climates and environments not represented in training data.

This unresolved issue affects the reliability of global-scale predictions, particularly for ungauged or remote basins with distinct hydro-climatic regimes.