A multi-resolution deep-learning surrogate framework for global
hydrological models
Abstract
Global hydrological models are important decision support tools for
policy making in today’s water-scarce world as their process-based
nature allows for worldwide water resources assessments under various
climate-change and socio-economic scenarios. Although efforts are
continuously being made to improve water resource assessments, global
hydrological model computational demands have dramatically increased and
calibrating them has proven to be difficult. To address these issues,
deep-learning approaches have gained prominence in the hydrological
community, in particular the development of deep-learning surrogates.
Nevertheless, the development of deep-learning global hydrological model
surrogates remains constrained, as previous surrogate frameworks only
focus on land-surface fluxes for a single spatial resolution. Therefore,
we introduce a global hydrological model surrogate framework that
includes integrated spatially-distributed runoff routing, human impacts
on water resources and the ability to scale across spatial resolutions.
To test our framework we develop a deep-learning surrogate for the
PCRaster Global Water Balance (PCR-GLOBWB) global hydrological model.
Our surrogate performed well when compared to the model outputs, with a
median Kling-Gupta Efficiency (KGE) of 0.45, while predictions were at
least an order of magnitude faster. Moreover, the multi-resolution
surrogate performed similarly to several single-resolution surrogates,
indicating limited trade-offs between the surrogate’s broad spatial
applicability and its performance. Model surrogates are a promising tool
for the global hydrological modeling community, given their potential
benefits in reducing computational demands and enhancing calibration.
Accordingly, our framework provides an excellent foundation for the
community to create their own multi-scale deep-learning global
hydrological model surrogates.