Using observed river flow data to improve the hydrological functioning of the JULES land surface model (vn4.3) used for regional coupled modelling in Great Britain (UKC2)

• Published in: Geoscientific Model Development Vol. 12; no. 2; pp. 765 - 784
• Main Authors: Martínez-de la Torre, Alberto, Blyth, Eleanor M., Weedon, Graham P.
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 20-02-2019; Copernicus Publications
• Subjects: Atmospheric models; Catchment area; Catchments; Climate models; Computer simulation; Dependence; Hydrologic data; Hydrologic models; Hydrology; Land surface models; Modelling; Moisture content; Ocean models; Parameter sensitivity; Parameterization; Parameters; Rain; Rainfall; Regions; River flow; Rivers; Runoff; Simulators; Soil; Soil conditions; Spatial discrimination; Spatial resolution; Spectral analysis; Stream flow; Surface runoff; Temperature (air-sea); Terrain; Terrestrial environments; Topography; United Kingdom > UK
• ISSN: 1991-9603; 1991-962X; 1991-959X; 1991-9603; 1991-962X
• DOI: 10.5194/gmd-12-765-2019

Land surface models (LSMs) represent terrestrial hydrology in weather and
climate modelling operational systems and research studies. We aim to improve
hydrological performance in the Joint UK Land Environment Simulator (JULES)
LSM that is used for distributed hydrological modelling within the new
land–atmosphere–ocean coupled prediction system UKC2 (UK regional Coupled environmental
prediction system 2). Using river flow observations from gauge stations, we
study the capability of JULES to simulate river flow at 1 km2 spatial
resolution within 13 catchments in Great Britain that exhibit a variety of
climatic and topographic characteristics. Tests designed to identify where
the model results are sensitive to the scheme and parameters chosen for
runoff production indicate that different catchments require different
parameters and even different runoff schemes for optimal results. We
introduce a new parameterisation of topographic variation that produces the
best daily river flow results (in terms of Nash–Sutcliffe efficiency and
mean bias) for all 13 catchments. The new parameterisation introduces a
dependency on terrain slope, constraining surface runoff production to wet
soil conditions over flatter regions, whereas over steeper regions the model
produces surface runoff for every rainfall event regardless of the soil
wetness state. This new parameterisation improves the model performance
across Great Britain. As an example, in the Thames catchment, which has
extensive areas of flat terrain, the Nash–Sutcliffe efficiency exceeds 0.8
using the new parameterisation. We use cross-spectral analysis to evaluate
the amplitude and phase of the modelled versus observed river flows over
timescales of 2 days to 10 years. This demonstrates that the model
performance is modified by changing the parameterisation by different amounts
over annual, weekly-to-monthly and multi-day timescales in different
catchments, providing insights into model deficiencies on particular
timescales, but it reinforces the newly developed parameterisation.