Anne Trainor
Geog 711
Exercise 1
Statement of the Problem
The objective of this modeling exercise was to examine how hydrology is influenced by soil
texture and depth. Specifically, I ran a 24 year simulation model with 3 different soil textures and 2 soil
depths forest ecosystem in Chapel Hill, North Carolina.
Experimental Setup
For this study, I used Biome-BGC Version 4.1.2 to simulate daily and annual variation in
hydrology. The spinup application was integrated to establish initial conditions for the simulation
model. Meteorology data is required for Biome-BGC simulations; therefore weather data collected in
Chapel Hill, NC from 1980 to 2003 were incorporated into the model. Once the initial conditions were
established from the spinup, I ran a 24 year simulation model that produced daily and annual output
from 1980 to 2003.
Six models were constructed during this exercise by running every combination of 3 soil types
and 2 soil depths, the treatment variables. The 3 soil types (loam, sandy, and clay) contained distinct
proportions of sand, silt, and clay (Table 1). The 2 soil depths evaluated for each soil type were 0.3 and
1 m. Hydrology can be examined through various metrics within the Biome-BGC modeling system. For
this exercise I focused on examining the following response variables for the annual output, maximum
Leaf Area Index (LAI), outflow, and evapotranspiration (ET). In addition, I evaluated 2 additional
variables, soil pressure (SoilPSI) and soil water, at a finer temporal resolution during a dry and wet year,
1988 and 1998, respectively.
Table 1. The combination of models simulated and proportion of soil composition for the 3 different soil
types to evaluate the influence of hydrology in Chapel Hill, NC.
Soil Depth (m) Soil Type
0.3
Sandy
Loamy
Clay
1.0
Sandy
Loamy
Clay
Soil Composition
SAND SILT CLAY
80
20
0
40
40
20
0
20
80
80
20
0
40
40
20
0
20
80
Results
The annual response the maximum LAI, outflow, and evapotranspiration did not vary greatly
between soil types when soil depth was modeled at 1 m. However, when soil depth was decreased to
0.3 m the maximum LAI for the sandy soil was consistently lower throughout all the years of the
simulations (Figure 1). Loamy soil produced the greatest maximum LAI values for the time span. The
overflow parameter for 0.3 m soil depth was relatively consistent for all the soil types with sandy soil
producing slightly more overflow throughout the simulated years. Evapotranspiration did not vary by
soil type or depth based on the results from the annual simulations.
The next step was to examine how hydrology is influenced by soil texture and depth at daily
time scale. I selected a dry and wet year, 1988 and 1998, respectively. During the dry year (1988), the
greatest difference observed in hydrology was noticed with soil pressure. The 1 m soil depth soil
pressure did not vary by soil type but the pattern throughout the year varied greatly when the soil depth
was reduced to 0.3 m (Figure 2). In addition, sandy soil contained significantly less soil pressure than
loam and clay soil. In July of 1988 the soil pressure dropped to approximately – 35 pa while loam and
clay soil pressure remained at approximately -4.5 pa. The same pattern for soil pressure was also
Anne Trainor
Geog 711
Exercise 1
observed during the wet year simulation. During the 1998, the lowest soil pressure for sand reached
approximately -33 pa in July while loam and clay remained at approximately -3.3 pa.
For dry and wet years soil water followed a similar pattern by soil type and depth. For 1988 and
1998, soil water varied greatly during the 1 m soil depth simulations by soil type. Clay contained the
greatest quantity of soil water and sandy soil contained the least amount of water. The soil water levels
at 0.3 m were much lower at 0.3 m soil depth and all 3 soil types contained approximately the same
quantity of soil water throughout the year.
Water overflow in the soils did vary between dry and wet years, significantly varied by soil depth
and slightly varied by soil type. No overflow resulted during the dry year when soil depth was set for 1
m for any of the soil types. However, when the soil depth was decreased to 0.3 m all soil types exhibited
overflow. The overflow occurred during 2 aggregated time periods, January to February and autumn
season. During these pulses sandy soil had the greatest amount of overflow. The daily variation in
overflow during the wet year also contained a similar seasonal pattern. However, there was no
significant difference between soil types or soil depth.
A similar LAI pattern was observed between dry and wet years for soil type and depth. In
addition, LAI was relatively consistent through out the year within soil type and depth. During the dry
year LAI varied by soil type for the 0.3 m soil depth. Loam contained the greatest LAI while clay and
sand contained the middle and lowest LAI, respectively. However, there was no variation between soil
types at 1 m soil depth during the dry year. In 1998, the wet year, LAI did not vary by soil type or soil
depth except for 1 case. Sandy soil with 0.3 m soil depth was significantly less throughout the wet year.
Evapotranspiration did not very by soil type or depth during the wet or dry years.
Figure 1. Maximum Leaf Area Index (LAI) simulated for 3 different soil textures at 0.3 m soil depth from
1980 to 2003 in Chapel Hill, NC.
Anne Trainor
Geog 711
Exercise 1
Figure 2. Daily soil pressure during 1988, a relatively dry year, simulated for 3 different soil textures at
(left) 1 m soil depth and (right) 0.3 m soil depth in Chapel Hill, NC.
Conclusion and Discussion
Soil texture and depth are important parameters when examining how an ecosystem responds
to hydrology. Biome-BGC allows researchers to evaluate how these parameters influence the water
balance and vegetation effects at an annual and daily time scale. Maximum LAI showed the greatest
response at the annual time scale for the shallower soil depth of 0.3 m but not at the deeper soil depth.
For the daily time scale I focused my attention on 2 years with extremely different amount of annual
precipitation. The dry year, 1988, contained 94.1 Cm yr-1 of precipitation while 1998 was considered a
wet year with 129.4 Cm yr-1 of precipitation. The greatest difference between these extreme years was
noticed through the soil pressure variable. Evapotranspiration did not vary greatly by soil type or depth
at the annual or daily time steps.
Soil texture influences water retention and availability for vegetation. For example, sandy soil
which has the greatest porosity, exhibited the least amount of LAI for the annual models and the lowest
water pressure for the daily models. Therefore, water is not storing in great quantities in the sandy soil
and as a result there is less available water for vegetation. Clay and loam soil textures have low levels of
porosity. Therefore, they are able to retain more water for storage but it can be more difficult for roots
to uptake the water from the soil.
Temporal scales can produce different insight into an ecosystem. For example, maximum LAI,
had the largest influence at the annual time step but during the daily time step LAI and
evapotranspiration were not greatly influenced by soil type and depth. This suggests that a process is
fluctuating in a system but the interaction with soil texture and depth may only be evident at a specific
temporal scale. These results also indicate the importance of selecting accurate site variables for soil
texture and depth when running predictive models. This is an important concern for land management
and planning operations since detailed soil composition and depths are usually deficient throughout the
country. Therefore, this modeling exercise demonstrated that it is important to test different soil
characteristics when information is uncertain or highly variable for a site.