4. Discussion
4.1
The C–G model reveals the hydrological processes
The non-stationary model of the C–G model is understood as a non-linear
model that can be applied to soils with closed isotope systems. The
variation of the isotopic composition of input soil water
(δP) and the isotopic composition of output soil water
(δL) in the C–G model reflects the course of soil water
composition in the environment at a given time and temperature and
humidity (Fig. 5). In continuous evaporation conditions and at high
frequencies of isotope measurements, evaporation exhibits a sinusoidal
response for different soil depths at the same time (Fig. 8). Variation
of evaporation f indicates that evaporation is not constant throughout
the year, and f does not evolve as a simple evaporation process but as a
competitive process of evaporation and isotopic mixing of multiple
reservoirs (isotopic recharge of upper or lower soil water and
evaporation of intermediate soil layers). In addition, the variation in
evaporation f in different seasons reflects different isotopic processes
- evaporation and mixing - that can be explained by different functions
(Fig. 8).
4.2 The accuracy of
the C–G model in quantifying soil evaporation
The lysimeter method is the only
physical evaluation method that can be used to measure changes in soil
moisture fluxes directly. Additionally, it can analyze the soil
evaporation pattern of water at various soil depths, for different soil
textures, and different crops growing under different climatic
conditions (Annelie et al. , 2021; Laura et al. , 2021).
However, the set water level in the lysimeter test is fixed, and a
deviation results from the natural condition of water level change or
the natural state of the water level rising and falling after continuous
evaporation. The formula method is
based on the observation results produced by the lysimeter; herein, the
empirical formula is analyzed, summarized, and then applied to the
estimation of soil evaporation without the consideration of associated
mechanism problems such as the movement of water during the soil
evaporation process; additionally, the result estimated by the
experimental formula of evaporation is still different from the actual
value (Lehmann et al. , 2019). The location flux method is based
on the theory of soil hydrodynamics and involves an analysis of the
evaporation process and calculation of the soil evaporation based on the
characteristics of the water potential distribution of soil (Xinget al. , 2019; Tingting et al. , 2021). Although the
physical interpretation of this method is clear, the applicable
conditions are relatively simple and differ significantly from the
complex and variable natural conditions; however, the observation
requirements for soil water potential are high, and the application of
this method is thus limited. The numerical simulation method is based on
a large amount of experimental data and combines the principles of
ground energy balance and soil hydrodynamics to establish a model for
simulating the soil water movement (Ma et al. , 2019; Li and Shi,
2021). The limitation of this method is that more parameters are
required for the calculation, and the uncertainty associated with the
model is also high. Furthermore, the accuracy of the model in estimating
the boundary conditions is limited. In contrast to previous research
methods, quantifying soil water evaporation using the C–G model is
theoretically applicable to any soil evaporation conditions.
Quantification of soil moisture evaporation using the C–G model was
similarly validated in the studies of Wei and Yong et al. (Wei et
al. , 2015; Yong et al. , 2020). Unlike Wei and Yong et al. We
studied a more specific case of soil evaporation estimation under
continuous evaporation conditions, which is critical for the study of
soil water resources in arid and semi-arid regions. Moreover, in this
study, we used soil water content data obtained simultaneously in an
experiment as a real measure of soil evaporation. We compared it with
the evaluation results of the C–G model, thus validating the accuracy
of the model (Fig. 8).
However, soil evaporation is influenced by several factors, such as
salinity (C.J. Barnes, 1988), temperature gradients (McDonnell, 1998),
soil-water transport mechanisms (Vincent Marc, 2001; Gazis and Feng,
2004), and soil layering (Brent D Newman, 1997). Sofer found that
salinity generally reduces equilibrium fractionation, and the degree of
reduction is dependent on the type of salt used. Indirectly, salinity
affects relative humidity, which reduces kinetic fractionation (Stewart
and Friedman, 1975; Z. Sofer, 1975). As Barnes found, temperature
affects evaporation of unsaturated soil water primarily by affecting
saturated water vapor density, which increases as temperature increases.
As a rule of thumb, equilibrium fractionation coefficients, effective
diffusion coefficients, and hydraulic conductivities cannot vary more
than a few percent per degree Celsius with temperature (C.J. Barnes,
1989). Padilla demonstrated that soil water transport mechanisms produce
different isotope profiles that alter soil evaporation calculations. In
unsaturated soils, piston flow is dominant when the water content is
high, while preferential flow is dominant when the water content is low
(Padilla et al. , 1999). Shurbaji found that clay layers possess
lower hydraulic conductivity than sand layers, allowing a greater
concentration of water to remain in clay layers, decreasing the rate of
isotope transport. This resulted in significantly different isotope
samples based on different soil layers (A.-R. Shurbaji, 1997). It is
still undetermined if these factors affect the calculation of the model
and if they can be used to modify the model. Thus, the manner in which
the interaction between the parameters affects the model is yet to be
determined. These issues should be addressed in a follow-up study.