New process descriptions
The study by Bronstert et al. (2023) focuses on description of
infiltration excess (Hortonian surface runoff) in catchment models. In
particular, they investigate the importance of micro- and macropores in
the infiltration process. The study is based on good datasets from well
instrumented infiltration/infiltration-excess experiments and
observations at three spatial scales: point, field and catchment (115
km2). Two modelling hypotheses were then tested
against these field data, namely an approach without macropores, which
is traditionally used in catchment models, and an approach based on
double-porosity soil enabling a combined modelling of high infiltration
rates in macropores and dampened soil moisture distribution after
termination of infiltration. The results from tests at point and field
scale suggest that both modelling approaches are capable of reproducing
soil moisture dynamics, but that the inclusion of macropores results in
more realistic soil hydraulic parameters. The results from catchment
scale show that the macropore based approach is more robust in
reproducing flood hydrographs for different rainfall intensities and
generally outperforms the modelling approach without macropores.
Altogether Bronstert et al. (2023) conclude that macropores are of high
relevance for infiltration and soil moisture dynamics during periods of
high intensity rainfall and therefore should be considered in catchment
modelling focusing on simulation of flood events.
While it is well known that stream discharge in vegetated catchments
during dry periods can exhibit natural fluctuations of up to 10% daily,
the capability of catchment models to reproduce such behavior and
explain the underlying processes has so far rarely been tested. Le
Cecilia et al. (2022) use the CATHY physically-based integrated
surface-subsurface hydrological model to study the complex processes
generating diel streamflow fluctuations in a 2.67 km2agricultural catchment in Switzerland. After demonstrating that the
model is capable of satisfactorily simulating the diel streamflow
fluctuations, including the timing of short-living streamflow peaks
attributed to irrigation, the model was subsequently used to test
alternative hypotheses for which processes may contribute to these
fluctuations. The results show that evapotranspiration is the dominant
process generating diel fluctuations, while changes in saturated
hydraulic conductivity due to diel soil temperature fluctuations caused
an amplitude of the diel streamflow signal 10 times smaller than
evapotranspiration.
The study by Riazzi et al. (2022) uses a travel time tracking method to
simulate stream electrical conductivity (EC) using high frequency
(hourly) monitoring data from the 369 km2 Duck River
catchment in Tasmania, Australia. Two modelling approaches are tested.
The first approach assumes that evapotranspiration is the only process
driving the changes in EC, while the second assumes that the water
salinity in catchment storages is a function of water age in these
storages. The results show that the two hypotheses are equally
successful in simulating EC concentrations and tracking its event and
seasonal dynamics, and hence it is not possible to differentiate which
of the two underlying hypothesis are better supported by the available
observational data. Given that EC data from operational observation
networks is much more widely available than other tracer data, Riazzi et
al. (2022) conclude that using EC data to calibrate travel time models
is a promising approach.
In contrary to other papers studying individual hydrological processes
or the impact of process descriptions on discharge or solute fluxes at
the catchment output, Gaur et al. (2022) evaluate how well a spatially
distributed hydrological model is able to reproduce observed spatial
patterns within the catchment. They use the MIKE SHE with 5 km x 5 km
resolution to simulate the hydrological response of the 19,276
km2 Subarnarekha catchment in Eastern India. The study
compares model simulations with remote sensing derived patterns for
evapotranspiration and soil moisture. The comparison is made using three
spatial performance metrics, i.e. joint empirical orthogonal functions
(EOF), fractional skill scores (FSS) and spatial efficiency (SPAEF). The
results demonstrate the potential and value of hydrological model
calibration to observed spatial patterns across time.