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.

References

However, the transferability of models trained in data-rich regions to unknown regions remains uncertain, especially when the characteristics of the climate, hydrology, and environment of the target location are not included in the training dataset.

— Approaches for enhancing extrapolability in process-based and data-driven models in hydrology  (2408.07071 - Shi, 2024) in Section 1 Introduction