Figure 10. The difference of GST (ΔGST) for each scenario
relative to L4 (left) and L6 (right). The first row for the year without
pumping while the second for the first year of pumping.
However, when GW pumping occurs, great difference of GST among scenarios
are observed not only in the first year of simulation (Figures 10b and
10d) but also through the entire simulation period (Figure 9). For
example, in Figure 9, the maximum increase of GST is about 4 K for L4
while it is less than 1 K for L14 in the first year of pumping (blue
lines). The difference can also be observed for the following years of
pumping (Figure 9). This indicates GW pumping significantly shortens the
time scale required to reveal the difference of GST caused by the
coupling depth in the integrated modeling. Therefore, with the
increasing intensity of human activities nowadays, the effect of
coupling depth on the heat components in land surface processes cannot
be neglected even in a short-term simulation. Such shortened time scale
also means weakened buffer capacity of the subsurface due to pumping. As
discussed in section 3.2, the thermal conductivity, the specific heat
capacity, and the volume to store the heat (e.g., the coupling depth)
are all important to the buffer capacity of the subsurface
(Cuesta-Valero et al., 2016). When simulating scenarios with pumping,
the decrease of thermal conductivity and volumetric heat capacity both
have negative effect on the buffer capacity, and thus the positive
effects of the increased coupling depth become more prominent than that
under natural conditions. The above explanations are also consistent
with results from the second group of simulations with tenfold pumping
rate that more significant effect of the coupling depth were shown
immediately after the pumping occurs, if comparing results presented in
Figures 5a and 5c.
- The root zones
The coupling depth between ParFlow and CLM determines the depth where
the root zone is truncated. This in turn provides the distribution of
root fraction in each layer which is important in the land surface water
and energy processes. Hence, the root zone for each scenario is analyzed
to help account for a portion of the different simulation results among
scenarios in section 3.3. The root fraction in each layer
(ri ) for the four land cover types (Figure 2b) is
calculated by Eq. (3) from the source code of ParFlow.CLM and is shown
in Figure 11,
(3)
where zh,i is the depth from the soil surface to
the interface between layers i and i +1
(zh, 0 = 0, the soil surface),Nlevsoi is the number of soil layers for
coupling, and ra and rbare plant-dependent root distribution parameters. Each land cover type
in this study corresponds to one plant functional type withra and rb based on IGBP
classification. In Figure 11, the root distribution for all plant types
can be fully described with a coupling-layer number larger than 10 since
the root fraction has exponentially decreased to zero. For L4, root
extension in the vertical direction for all plant types are truncated.
For L6, L7, and L8, slight truncation may be needed. For L11 and L14,
the root zone should be fully described. Therefore, obvious difference
for water and energy components is expected between L4 and other
scenarios (Figure 10). However, it should be noted that deeper
truncation does not mean better parameterization of the root-fraction
distribution.