Plain Language Summary
Hydrological models need information about the soil physical
characteristics (soil hydraulic parameters), which are in general not
available if the models are applied at larger scales (region to global
scale). Therefore, pedotransfer functions (PTFs) are classically used,
which relate easily available soil properties such as sand-, silt-,
clay-content, soil organic carbon content, and soil bulk density, which
are available from soil maps, to the soil hydraulic parameters.
Unfortunately, there are many different PTFs available in literature. In
the study presented, we analysed the impact of different PTFs on the
simulation results of water fluxes and found, that the choice of PTF
impacts the simulation results. Further, some PTFs were identified as
being less robust compared to others. In general, the study shows that
harmonizing PTFs in model-inter-comparisons is needed to avoid artefacts
originating from the choice of PTF rather from different model
structures.
Keywords: pedotransfer functions, land surface models, LSM,
hydrological models, crop models, model inter-comparison, model ensemble
mean
Introduction
Water fluxes and soil water content are key variables in the terrestrial
system as they control the exchange of water and energy between the
land-surface and the atmosphere (e.g., Vereecken et al., 2015).
Modelling of the water flow in the unsaturated zone, and the uncertainty
in the parameters used to simulate water flow, has been a topic of
intense research for many years, both in the soil hydrological and land
surface modelling community (Shao & Irannejad, 1999; (Tietje &
Tapkenhinrichs, 1993; Vereecken et al., 2008; Iwema et al. (2017)).
Moreover, climate modellers have studied the role of soil water content,
and strongly related processes such as evapotranspiration, in climate
and atmospheric processes (Koster & Suarez 2001; Ek & Holtslag, 2004;
van den Hurk et al., 2008; Seneviratne et al., 2010; Groh et al., 2020).
In this context, we require reliable estimates of soil hydraulic
properties at point to global scale (Cornelis et al., 2001, van Looy et
al., 2017). Measuring these properties is tedious, time and cost
expensive, and prone to measurement errors. Often, taking measurements
is not feasible due to the complexity and/or size of the terrestrial
system under investigation. To overcome this problem, pedotransfer
functions (PTFs), which estimate these essential soil properties from
easily available soil parameters, such as soil texture, soil structure,
bulk density, and soil organic matter, have been developed. An extensive
overview of existing PTFs was provided by Vereecken et al. (2010) and by
van Looy et al. (2017).
Soil properties used as basic input data to estimate the soil hydraulic
properties with PTFs are grouped into four categories: (1) soil particle
size or soil texture, (2) easily measurable hydraulic properties, (3)
morphological properties, and (4) chemical properties (Espino et al.,
1995, Vereecken et al. 2010; van Looy et al. 2017; Rahmati et al., 2013;
Neyshabouri et al., 2015; Rahmati and Neyshabouri, 2016). In general,
two different types of PTF can be distinguished, namely point and
parametric PTFs. Point PTFs estimate soil water content (or hydraulic
conductivity) values at predefined pressure head values (e.g., field
capacity or wilting point), whereas parametric PTFs provide the
parameters than can be used in hydraulic functions (water retention
curve (WRC) and hydraulic conductivity curve (HCC)) of the Brooks &
Corey (1964) or van Genuchten (1980) formulation. The most useful PTFs
developed in recent years are the parametric PTFs because they can be
used to calculate the WRC and HCC, which are used to simulate the water
fluxes in the numerical models. Secondly, PTFs are classified into class
and continuous PTFs. Class PTFs are look up tables, where the hydraulic
parameters are listed for typical soil textural classes (e.g., 12 USDA
soil classes). Continuous PTFs, on the other hand, use mathematical
descriptions, e.g., regression functions, to calculate the hydraulic
parameters from the entire range of data inputs like e.g. soil texture.
It has been shown that PTFs are highly accurate for the area (or the
input data range) they were developed for, but have limited accuracy if
applied outside these regions (Vereecken et al., 2010). Several reviews
about the accuracy and reliability of PTFs for the van Genuchten model
(VGM) have already been published (e.g., Wösten et al., 2001; Schaap,
2004; Donatelli et al., 2004). Hereby, the predicted hydraulic function
from the PTFs were compared to the measured data and the goodness of fit
of the prediction was evaluated. The authors used two metrics to
determine the performance of the PTF: 1) the term accuracy was related
to the comparison between predicted and measured values of water content
or hydraulic conductivity that were used to develop the PTF; 2)
reliability was related to the evaluation of PTFs on measured values
that were different from those that were used to develop the PTFs
(Wösten et al., 2001, Zhang et al., 2020). Reliability studies are
typically validation studies such as those performed by Tietje &
Tapkenhinrichs (1993), Wösten et al. (2001), and Wagner et al. (2001).
Despite much progress in developing PTFs and in identifying appropriate
PTF predictor candidates, some unresolved or unexplained variability
still exists at the level of the soil sample (Schaap & Leij, 1998),
which plays and important role when functional aspects of soils are
being studied and analysed using numerical models (e.g., Christiaens &
Feyen, 2001). Functional aspects already studied are the impact of PTFs
on water supply capacity (Vereecken et al., 1992), ground water recharge
(Vereecken et al., 1992), and aeration (Wösten et al., 2001). In the
study of Vereecken et al. (1992), the authors showed that 90% of the
variation in the predicted soil water supply was attributed to
estimation errors in hydraulic properties using the PTFs developed by
Vereecken et al. (1989, 1990). Chirico et al. (2010), on the other hand,
evaluated the effect of PTF prediction uncertainty on the components of
the soil water balance at the hillslope scale. One major result was that
the simulated evaporation was much more affected by the PTF model error
than by errors resulting from uncertainty in the input data (e.g., soil
texture).
Land surface models (LSMs), when embedded in numerical weather
prediction or climate models, generally operate at large scales
(regional, continental to global scales) and rely on PTFs to predict the
hydraulic functions needed to solve the Richards equation for the water
flow. Different LSMs use different PTFs for this purpose (Vereecken et
al., 2019). As Vereecken et al. (2019) showed, not only different PTFs
but also different hydraulic models (Campbell, Brooks and Corey, or
Mualem-van Genuchten) are in use. Knowing that different PTFs and/or the
choice of the hydraulic model will impact the outcome of the water flow
simulations (e.g., Gruber et al., 2006; Yakirevich et al., 2013), a key
question is how recently launched LSM inter-comparison activities of the
Land Surface Schemes (LSSs) embedded in LSMs, such as those under the
Global Energy and Water Exchanges (GEWEX) GLASS project
(https://www.gewex.org/panels/global-landatmosphere-system-study-panel/)
or model inter-comparisons initiated by the World Climate Research
Programme (https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6, of
which GEWEX is part), such as CMIP6 and its predecessors, will be
impacted by the choice of PTF.
Therefore, the aim of this study is to systematically analyse the
functional sensitivity to the choice of different PTFs using a
physically-based numerical model. As the ‘truth’ of this model exercise
is unknown, the performance of the model runs with its hydraulic
parameters derived from a set of individual PTFs will be evaluated
against the ensemble mean as best predictor, as well as against the 70
and 90 tolerance intervals of the ensemble range. The numerical exercise
is structured in the following way: 1) model runs for homogeneous soil
profiles without vegetation, 2) homogeneous soil profile covered with
grass and wheat, 3) layered bare soil, 4) layered vegetated soil (grass
and wheat), and 5) influence of a fluctuating water table in a layered
grass vegetated soil. Finally, additional soil physical properties were
calculated based on the estimated soil hydraulic parameters obtain from
the PTFs, which were used to explain the differences observed in
simulated water fluxes. As some LSMs also use class PTFs (van Looy et
al., 2017, Vereecken et al., 2019), we will also analyse the use of this
type of PTF, and the associated errors when simulating water fluxes. We
formulate three hypotheses 1) the use of different PTFs will lead to
systematically different hydrological states and fluxes (e.g., net
infiltration, evapotranspiration, root zone water availability,
drainage), 2) some PTFs can be identified which perform distinctively
differently from the ensemble spread in terms of 90 % tolerance
interval outliers, and 3) the differences in predicted states and fluxes
simulated with inputs from different PTFs will be reduced with
increasing model setup complexity.
Materials and Methods