Spatially-varying parametrization of the Total Runoff Integrating Pathways (TRIP) scheme for improved river routing at the global scale

Tsilimigkras, Athanasios A. ORCID: https://orcid.org/0000-0002-8443-6420; Clark, Douglas B. ORCID: https://orcid.org/0000-0003-1348-7922; Hartley, Andrew J. ORCID: https://orcid.org/0000-0002-1905-9112; Burke, Eleanor J.; Grillakis, Manolis G.; Koutroulis, Aristeidis G.. 2025 Spatially-varying parametrization of the Total Runoff Integrating Pathways (TRIP) scheme for improved river routing at the global scale. Journal of Hydrology, 133477. 10.1016/j.jhydrol.2025.133477

Full text not available from this repository.

Official URL: https://doi.org/10.1016/j.jhydrol.2025.133477

Abstract/Summary

•Land Surface Models (LSMs), such as the Joint UK Land Environment Simulator (JULES), are pivotal in simulating biophysical processes on Earth’s surface, with key applications in assessing ecological impacts of climate change on hydrology. However, both model structural and parametric limitations persist in aspects like surface runoff routing, where the Total Runoff Integrating Pathways (TRIP) scheme used in JULES typically represents no sub-grid variability in river pathways by parametrizing uniform velocity and meander ratios. These uniform in space parameters often fail to capture the diverse hydrological characteristics of different river systems, leading to inaccuracies in predicting river flow patterns, especially in regions with complex topographical and hydrological dynamics. •This study addresses this limitation by introducing a spatially varying parameterization scheme for these parameters, using data from 360 river basins around the globe. The optimization involved a factorial experiment, exploring different velocity and meander ratio setups. A function-fitting neural network was employed to correlate the optimal parameters with river basin physiographic attributes, utilizing data from the GRDC and HydroATLAS databases. The neural network’s results were then extrapolated globally. •Results showed that the introduction of a new river routing configuration in the JULES model significantly improved the accuracy of river flow simulations, notably in capturing temporal flow dynamics. This led to an average increase of 0.10 in 10-day averaged Nash-Sutcliffe Efficiency across the 360 river basins. Additionally, this research has contributed to a new version of JULES, incorporating spatially varying routing parameters, marking a shift from the traditional fixed parameter approach. Our assessment revealed that with this updated configuration, about 75% of the total river area evaluated showed enhanced simulation performance compared to the default settings, marking a paradigm shift in LSMs for improved performance at the local conditions.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.jhydrol.2025.133477
UKCEH and CEH Sections/Science Areas:	Water and Climate Science (2025-)
ISSN:	0022-1694
Additional Keywords:	large-scale hydrological modelling, land surface models (LSMs), river routing, hydro-environmental basin characteristics, JULES
NORA Subject Terms:	Hydrology
Date made live:	14 May 2025 07:14 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/539435

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.jhydrol.2025.133477

UKCEH and CEH Sections/Science Areas:

Water and Climate Science (2025-)

ISSN:

0022-1694

Additional Keywords:

large-scale hydrological modelling, land surface models (LSMs), river routing, hydro-environmental basin characteristics, JULES

NORA Subject Terms:

Hydrology