Description
Critical zone structures e.g., topography, soil and bedrock, are the
first-order control in shaping runoff generation in headwater catchments
(Zimmer et al., 2017; Harman & Kim, 2018; Anderson et al., 2019; Liu et
al., 2019; Fan et al., 2020). But the critical zone beneath our feet,
where subsurface flow moves, is invisible, and until recently, it is
still an inaccessible and unknown world (Grant & Dietrich, 2017). The
reason is mainly that the underlying surface of the watershed has huge
heterogeneity at different scales. Meanwhile, it is hard to characterize
subsurface flow path of transient runoff (Weiler et al., 2006). This
explains why classroom teaching and short-term field trips are difficult
to reproduce runoff formation in the critical zone at a glance view.
Contrary to field research, physical model is a practical means for
theoretical verification and law discovery (Black, 1970; Etkina et al.,
2002). Particularly, the fully controlled models with expected terrain
and soil properties may have great potential to deepen our recognition
of critical zone (Kleinhans et al., 2010). To our knowledge, soil trough
with variable slopes has been widely accepted to investigate
hydrological processes by many hydrologists. For example, the
slope-variable soil trough in Hohai University comprises two contrast
tanks to study the effects of vegetation cover on the runoff response
(Song & Wang, 2019). In contrast to above soil troughs without
considering plane shape, LEO is more favored by scholars for its
convergent topography (Hopp et al., 2009; Gevaert et al., 2014). Even
LEO is only a simple morphology, how to model the functions and
structures of real-world catchments have been still a key difficulty for
physical model developments. Since, as reported by Gevaert et al.
(2014), the structures as well as functions can be ruined by an
unintended gully erosion through a single heavy rainfall. And numerous
studies have also shown that erosion of backfilling soil, caused by
rainfall and overland flow, is a very common phenomenon in laboratory
experiments (Bryan & Luk, 1981; Jomaa et al., 2010; Ran et al., 2012).
This grand challenge is inhibiting physical models from developing
variable and desired morphologies that reflect complex characteristics
of critical zone.
To further facilitate education and research about the role of critical
zone, a physical model with complex terrain has been built. The key of
the model is to abandon the traditional backfilling soil and then seek
for permeable material. Currently, permeable bricks made of fine
aggregate (sand), coarse aggregate (gravel) and cement have been widely
used for pavements to allow rainwater to quickly seep into the
underground in the field of Low Impact Development (LID) (Dietz, 2007;
Ahiablame et al., 2012; Eckart et al., 2017). The aim of the
permeable
materials is not only to strengthen the capability of infiltration, but
also to enhance compressive strength and bending strength at the same
time (Nishigaki, 2000; Poon & Chan, 2005; Debnath & Sarkar, 2019).
Their maximum water holding capacity is about 13% (Wang et al., 2018),
far smaller than that of the natural soils. Generally, the saturated
hydraulic conductivity (Ksat) of materials is about one to three orders
of magnitude higher than that of the natural soil (Wu et al., 2016;
Zhou, 2018; Tang et al., 2019) for the reason that its principal
particle components are far coarser than those of the natural soils.
According to the suggested ratios, aggregate accounting for about 70%
of total volume of concrete (Cai et al., 2018) and water cement ratio
ranging from 0.3-0.4 (Debnath & Sarkar, 2019; Rahmani et al., 2020)
were used in this study. But the fine aggregate ratio is increased to
about 0.8 for weakening the permeability. Three cases with different
aggregate cement ratios were tested by altering the bulk density of the
mixed material (Tab. 1). Ksat was selected to be the only hydraulic
indicator because it plays the key role in the seepage process (Chapuis,
2012). According to the test results (Fig. 1), the permeability of the
materials matches that of the natural soil closely, while the field
moisture capacity (FMC) is close to loam according to Field Estimation
of Soil Water Content (2008). In addition, it is found that Ksat and FMC
are both well correlated with bulk density (BD) in these three cases. In
other words, the values of Ksat and FMC can be controlled through
changing BD values of the mixed materials. Finally, the fitting curves
in Case 2, i.e., the stronger nonlinear correlation
(R2=0.75) between Ksat and BD (Sriravindrarajah et
al., 2012; Kevern et al., 2014; Debnath & Sarkar, 2019), were adopted.
The prototype of the physical model is a steep 0.31-ha zero-order basin
(hereafter referred to as H1), which is located within the Hemuqiao
Hydrological Experimental Stational Station (30°34’ N, 119°47’ E; 135
ha) in Taihu Basin in southeastern China (Han et al., 2020). For the
convenience of teaching and construction, the horizontal scale ratio
between the model and H1 is 1:130 and the vertical scale ratio is 1:30.
The exact measurements of the employed physical model are 6.2 m (length)
× 3.9 m (width) × 2 m (height) (Fig. 2a). The model is located in a
meteorological observation field for hydro-meteorological education. The
model mainly comprises impermeable layer paved by concrete and two
artificial soil layers (Fig. 2b). These two artificial soil layers were
made of a selected material according to the relationship of Ksat-FMC-BD
(Fig. 1b). The two artificial soil layers are filled homogeneously,
whose thickness ratio between the upper and lower layers are consistent
with the prototype, and the thickness are 8 cm and 24 cm, respectively.
The corresponding Ksat values are 1.4 mm/min and 0.2 mm/min, which are
approximately the average values of the upper and lower soils in H1.
However, the FMC values in the upper and lower layers are around 20%
and 14%, of which the lower value is 18% less than real-world value.
The reason is that the proportion of the components in the selected
material stays the same but clay content in the lower soils in H1 is
increased. Over the artificial soil layers, fake turf was paved for the
case of raindrops splashing down and direct sunlight (Fig. 2b).
In the physical model, various processes in both natural and artificial
rainfall-runoff events can be monitored (Fig. 2c&d). For the purpose of
education and research, four projects have been established. First, 12
groundwater wells are set to observe how free water changes at different
locations on the hillslope model, how the wells respond to rainfall
process, and how topography and media affect the storage and discharge
of free soil water (McMillan & Srinivasan, 2015; Han et al, 2020).
Second, in order to understand changes of soil temperature and moisture
content, TDR probes are vertically inserted into the upper artificial
soil layer. Third, at the outlet (Fig. 2d), there is a weir (Han et al.,
2016) that can simultaneously observe surface and subsurface runoff.
Finally, two cameras are used to cover all possible positions that
generate runoff during rainstorms.
We usually present the physical model at natural rainfall events. After
a rainfall-runoff event, the maintainer could be asked to collect all
data. Then the continuous time series would be stored for hydrological
characteristic analysis of the model. In summary, it provides us with an
efficient tool to identify the role of critical zone structures in
shaping streamflow. The artificial soil has controllable hydraulic
properties for permeable layers in the model, which is of potential to
replace the real-world soils. More importantly, compared to the
backfilling soil, it resists erosion and is not easy to deform so as to
promote the development of the physical models with complex terrain.