1. National scale hydrological modelling for the UK
Rosie Lane, Jim Freer and Thorsten Wagener
Introduction
Applying Dynamic TOPMODEL for the river Severn - for high flow/ flood simulation
Data and hydrological models are now available for national hydrological analyses.
I have been working on two areas relating to national-scale hydrological modelling:
Benchmarking conceptual hydrological model performance for catchments across the UK.
Automated application of a Dynamic TOPMODEL for the river Severn – with a view to applying Dynamic TOPMODEL nationally to assess flood hazard in the future.
Dynamic TOPMODEL is a semi-distributed hydrological model, developed within a flexible modelling framework.
I aim to apply this model nationally to assess climate change impact on flood hazard, but first I am applying the model over the Severn catchment to explore:
Evaluation of model performance for a large and varied UK catchment.
Implementation of an automated parameterization scheme which is spatially coherent and could be applied to parameterize a national model.
Digital Terrain Analysis for the river Severn:
Automated digital terrain analysis has been carried out for the river Severn to identify HRUs, prior to running the model.
Benchmarking conceptual model performance across the UK
This work looked at how well simple, lumped hydrological models performed across the UK, identifying regions where models consistently failed to produce good results and exploring the influence of catchment characteristics and model structural choices on model performance. I will extend this to look at model predictive capability for annual maximum flood peaks.
Methods
Four lumped hydrological models from the FUSE framework were applied to 1128 UK catchments, over the period
Models applied using the GLUE uncertainty analysis framework.
Results
Results can be seen in Figures 1 and 2. It was found that:
There is an east/west divide in model performance.
Clusters of poorly simulated catchments were identified around the east of England and central Scotland, where all models fail.
No one model is best for all catchments: relative model performance varies depending on catchment BFI among other characteristics.
Accumulated
area
Slope
Topographic
index
Rainfall
input grids
(10km or uniform)
Catchment
masks
HRUs
(3210 total)
Digital terrain analysis was carried out using both 10km rainfall input (3210 HRUs) as shown above, or uniform rainfall input across the Severn (573 HRUs).
Initial Results: Dynamic TOPMODEL performance
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Nash-Sutcliffe Efficiency
Figure 3: Initial performance of Dynamic TOPMODEL across the Severn. Map shows max NSE gained running the model with 100 random parameter sets for the year of Time-series are shown for 3 example catchments.
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Figure 1. Performance of 4 hydrological models applied within the FUSE framework across >1000 UK catchments. Each circle represents a catchment, coloured by the best NSE score obtained from 10,000 parameter sets.
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Next Steps
Currently, calibration of Dynamic TOPMODEL is carried out using Monte Carlo simulations, but this is infeasible on a national scale.
I next plan to implement Multi-scale parameter regionalisation, a spatially coherent parameterisation method, across the Severn catchment.
Figure 2. Relative model performance for UK catchments with varying BFI values. Each line represents all behavioural (NSE>0.5) model simulations for a specific catchment, coloured by the proportion of those behavioural simulations that came from each model.
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% of total behavioural simulations
Connect with me (Rosie Lane) on LinkedIn
Increasing catchment BFI 