Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain

Abstract

Abstract. Benchmarking model performance across large samples of catchments is useful to guide model selection and future model development. Given uncertainties in the observational data we use to drive and evaluate hydrological models, and uncertainties in the structure and parameterisation of models we use to produce hydrological simulations and predictions, it is essential that model evaluation is undertaken within an uncertainty analysis framework. Here, we benchmark the capability of several lumped hydrological models across Great Britain by focusing on daily flow and peak flow simulation. Four hydrological model structures from the Framework for Understanding Structural Errors (FUSE) were applied to over 1000 catchments in England, Wales and Scotland. Model performance was then evaluated using standard performance metrics for daily flows and novel performance metrics for peak flows considering parameter uncertainty. Our results show that lumped hydrological models were able to produce adequate simulations across most of Great Britain, with each model producing simulations exceeding a 0.5 Nash–Sutcliffe efficiency for at least 80 % of catchments. All four models showed a similar spatial pattern of performance, producing better simulations in the wetter catchments to the west and poor model performance in central Scotland and south-eastern England. Poor model performance was often linked to the catchment water balance, with models unable to capture the catchment hydrology where the water balance did not close. Overall, performance was similar between model structures, but different models performed better for different catchment characteristics and metrics, as well as for assessing daily or peak flows, leading to the ensemble of model structures outperforming any single structure, thus demonstrating the value of using multi-model structures across a large sample of different catchment behaviours. This research evaluates what conceptual lumped models can achieve as a performance benchmark and provides interesting insights into where and why these simple models may fail. The large number of river catchments included in this study makes it an appropriate benchmark for any future developments of a national model of Great Britain.

A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments

“…This model is the MARRMoT version of the Xinanjiang model Zhao, 1992), modified with a unique feature not seen in any other model in our sample, namely a double parabolic curve that is used to represent the fraction of the catchment that contributes to free drainage (Jayawardena & Zhou, 2000). Nonlinear treatment of saturated area representation has been linked to more flexible model performance within groundwater-dominated catchments before (Lane et al, 2019) and we can speculate that this specific double parabolic formulation gives the model a unique capability that allows it to perform well in a wide variety of catchments. Interestingly, it is difficult to generalize these findings because for every model (including m28) certain catchments can be found where that model is one of the best structures (in terms of efficiency scores during evaluation) and equally catchments can be found where that model is one of the worst options (Figure 9 Perrin et al, 2001, presents a similar finding).…”

Flood hazard and change impact assessments may profit from rethinking model calibration strategies

“…A reliable model ideally reproduces different aspects of flooding, including local characteristics such as event magnitude and timing. It has been shown, however, that capturing magnitude and timing is challenging when standard calibration metrics are used individually for parameter estimation (Lane et al, 2019;Brunner and Sikorska, 2018;Mizukami et al, 2019). For example, one widely-used metric that is considered integrative compared to others (e.g., bias, correlation) is the Nash-Sutcliffe efficiency (E NS ; Nash and Sutcliffe 1970), but it is formulated so that its optimal value actually underestimates flow variability (Gupta et al, 2009).…”

Grappling with uncertainties in physical climate impact projections of water resources

“…Although still based on empirical models, such estimates may provide a useful benchmark to evaluate the performance of GHMs. Nevertheless, we need to keep in mind that all observational data are uncertain (Beven et al 2019) and it is therefore essential that model evaluation is undertaken within an uncertainty analysis framework (Lane et al 2019).…”

“…First of all, we feel that the community will benefit from developing a common language and clear framework for the treatment of uncertainties in climate impact assessments. For model development, there is a need to explore parameter and structural uncertainty in a more consistent manner, akin to the perturbed parameter ensembles used in climate modelling and the approach used by Lane et al (2019) for river flow and flood prediction in Great Britain, as opposed to the current "ensembles of opportunity." At the same time, MIPs could be exploited more fully to better understand the mechanisms and processes that lead to different responses in the models and could explain part of the uncertainty in climate impact projections.…”