Exploring multiple model
structures
Astagneau et al. (2022)
hypothesize that the response of a catchment to high-intensity rainfall
events is highly heterogeneous due to complex interactions among the
hydrological processes at short temporal and spatial scales. The aim of
their study is to improve the simulation of summer floods by using a
lumped conceptual rainfall–runoff model. They modify the GR5H model and
test three hypotheses: i) large rainfall intensities increase the volume
of effective rainfall, ii) large rainfall intensities induce a faster
routing of effective rainfall to the catchment outlet, and iii) a
combination of these two hypotheses. A large database consisting of
10,652 flood events in 229 French catchments are used. The results show
that when the storages and fluxes of a lumped conceptual model
dynamically depend on rainfall intensities, the errors in flood volume
are less (at least in simulations at hourly time step). It is noted that
these conclusions specifically hold good for a particular model
structure and further testing with other models and the data from other
regions would be required to establish the wider applicability. Since
intense rainfall events do not last long, the intensity-dependent
functions are triggered for very small number of time steps. To address
the calibration issues arising due to the above hypotheses, Astagneau et
al. (2023) suggest regionalizing the parameters of the
intensity-dependent function.
Saavedra et al. (2022) investigates if hydrological consistency in
contrasting climate periods can be improved by sampling the model space
with a simple pareto framework and if such a model selection procedure
can reduce uncertainties in precipitation elasticities and temperature
sensitivities. They use the Framework for Understanding Structural Error
(FUSE) to produce 78 different hydrological model structures from four
different conceptual parent models. To test the ability of models to
predict impacts of climate change, they perform differential
split-sample tests of the models by calibrating on dry periods and
evaluating on wet periods and vice versa. The models are tested on three
catchments in Peru with areas ranging from 3545 km2 to
9586 km2. The results show that it is possible to
identify some model structures that robustly simulate catchment-scale
hydrology under different climate conditions, and that these are not
necessarily the structures that perform the best for traditional
efficiency metrics. The results also show that the model selection
procedure resulted in a significant reduction in the spread in
precipitation elasticities and temperature sensitivities.
Sinha et al. (2022) perform an intercomparison test of the GR4J lumped
conceptual model against the spatially distributed mHM model using data
from 50 catchments in UK. The models are calibrated by optimizing the
Nash-Sutcliffe efficiency. Subsequently, the model performances in
validation periods are evaluated by four performance metrics as well as
five hydrological signatures characterizing the ability of the models to
reproduce different components of the flow. The results support previous
findings that a lumped conceptual models can perform equally well, and
in some cases slightly better, than a more complex model, when the
modelling objective is limited to discharge simulation.