Model calibration and improved process
understanding
Beven et al. (2022) present a novel invalidation approach to calibrate
an ensemble of Dynamic Topmodel parameter sets in a study examining the
potential for hillslope storage bunds to mitigate the effects of
downstream flooding in the 209 km2 River Kent
catchment in UK. The model invalidation approach is based on a GLUE-like
methodology, where the acceptability thresholds in a first
goodness-of-fit step is defined to reflect the uncertainty associated
with input and discharge data. 118 realizations out of 100,000 survived
evaluations of hydrograph peaks in three years with major floods. While
most model calibrations are confined to such goodness-of-fit measures of
how well models perform in discharge simulations, Beven et al. (2022)
introduced an additional evaluation step, where only the simulations
with at least 10% of the area producing overland flow during the
largest storm were accepted. This fitness-for-purpose measure reduced
the acceptable realizations to 67. Altogether, the 67 surviving
realizations are not necessarily those that give the highest
Nash-Sutcliffe efficiency values, but those that are considered most
suitable for assessing the impact of certain flood mitigation measures
in the catchment.
De Lavenne et al. (2022) use the HYPE model for 111 catchments spread
across the USA to evaluate the effect of calibration against both
discharge and sediment data instead of only discharge data and to
evaluate five hypotheses for overland flow process descriptions. The
results confirm previous findings that inclusion of a second data set
(in this case sediment) in a multi-objective calibration approach
generally lead to significantly improved simulations for sediment
concentrations with only a slightly reduced performance for discharge.
The five overland flow modelling hypotheses consist of the existing
formulation using three parameters and four new formulations using one
or two parameters. The results show that the performance for discharge
simulations is not improved by the new hypotheses, while the
performances for sediment concentrations are improved. In addition,
equifinality is reduced by the new hypothesis due to a lower number of
model parameters.
La Folette et al. (2022) study streamflow simulation in the 16.9
km2 Elder Creek catchment in Northern California,
where the geology is characterized by fractured bedrock overlain by a,
typically thin (0.5 m), soil layer. This is the first study where
unsaturated weathered bedrock water storage is explicitly incorporated
in a catchment model and used as a calibration target. They calibrate a
lumped rainfall-runoff model against three observations targets: i) only
streamflow data; ii) only rock moisture data; and iii) both streamflow
and rock moisture data. The calibration is performed by evaluating
10,000 parameter sets using the concept of pareto optimality. The
results show that the model calibrated against both streamflow and rock
moisture data is capable of accurately simulating the dynamics in rock
moisture and streamflow, while a calibration against streamflow data
alone may result in relatively poor simulation of rock moisture dynamics
and a calibration against rock moisture alone may result in relatively
poor simulation of streamflow dynamics. Furthermore, the results show
that the calibrated parameter values appear more physically realistic
when calibrating against both streamflow and rock moisture data. The
study concludes that incorporation of rock moisture data can lead to a
more robust model, that without sacrificing the accuracy of streamflow
simulations can provide increased accuracy of some model results and
decreased parameter uncertainty.