Modelling Chlorine Transport in Temperate Soils Olatunde Idris Ibikunle

Modelling Chlorine Transport in Temperate Soils  Olatunde Idris Ibikunle
The Tema Institute
Campus Norrköping
Modelling Chlorine Transport in
Temperate Soils
Olatunde Idris Ibikunle
Master of Science Thesis, Environmental Science Programme, 2007
Linköpings universitet, Campus Norrköping, SE-601 74 Norrköping, Sweden
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Date
2007-06-15
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Department, Division
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Miljövetenskap
The Tema Institute
Environmental Science
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Svenska/Swedish
Engelska/English
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Author
Olatunde Idris Ibikunle
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Abstract
Microbes have been suggested to have a strong impact on the transportation of chlorine in soils. There are speculations about environmental
factors limiting microbial effect on chlorine movement and retention. For this study, a numerical hydrochemical model was built to describe
microbial transformation of chlorine in a laboratory lysimeter experiment. Undisturbed soil cores used to set-up the experiment were collected
from a coniferous forest soil in southeast Sweden. The lysimeters were modelled in groups depending on their different water and chloride
treatments. Microbial transformation of chlorine was better described under high water residence times and high chloride loads compared to low
water residence times and low chloride loads. Microbial activity was also shown to properly account for a sudden shift from net-chlorine retention
to net chlorine release in most of the lysimeters. Oxygen proved to be very important in accounting for the short-term shift from chloride retention
to release in all the lysimeters. Model outcome revealed that 0.02– 0.10 mg Cl- could be available per day in a coniferous soil depending on
season and other soil conditions. This study shows that modeling enable a better understanding of chlorine biogeochemistry. It also confirms the
speculated importance of microbial activities on chloride availability in soils.
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Keywords
Hydrochemical model, chlorine biogeochemistry, microbial retention and oxygen
ABSTRACT
Microbes have been suggested to have a strong impact on the transportation of
chlorine in soils. There are speculations about environmental factors limiting
microbial effect on chlorine movement and retention. For this study, a numerical
hydrochemical model was built to describe microbial transformation of chlorine in a
laboratory lysimeter experiment. Undisturbed soil cores used to set-up the experiment
were collected from a coniferous forest soil in southeast Sweden. The lysimeters were
modelled in groups depending on their different water and chloride treatments.
Microbial transformation of chlorine was better described under high water residence
times and high chloride loads compared to low water residence times and low
chloride loads. Microbial activity was also shown to properly account for a sudden
shift from net-chlorine retention to net chlorine release in most of the lysimeters.
Oxygen proved to be very important in accounting for the short-term shift from
chloride retention to release in all the lysimeters. Model outcome revealed that 0.02–
0.10 mg Cl- could be available per day in a coniferous soil depending on season and
other soil conditions. This study shows that modeling enable a better understanding of
chlorine biogeochemistry. It also confirms the speculated importance of microbial
activities on chloride availability in soils.
Keywords: Hydrochemical model, chlorine biogeochemistry, microbial retention and
oxygen.
1
ACKNOWLEDGEMENT
First, I want to appreciate the guidance and blessings I have been bestowed by the
Almighty from cradle till present. He has provided me the wherewithal that have been
used to prosecute existence through the hard and very difficult times fate’s put me
through.
My sincere appreciation goes to my supervisor, Ass. Prof. Per Sanden who has always
been there beyond the walls of the class room. I hope your shoulders will still be
available anytime it’s requested. My sincere gratitude also goes to Dr. Frank Laturnus
who has always provided ever-willing assistance in times of need. The same order of
gratitude goes to all my tutors and guidance through the environmental science
programme. I beg your forgiveness for my inability to equate thanks with your
kindness.
My thanks also goes to the Swedish government, for providing the rare opportunity of
studying on free tuition, your gesture has been a good pedestal on which my floating
dreams have lived.
To Stephen, my project mate, the times we spent scrolling through those multi-rows
of data will never be forgotten. I pray that the calibrated values of our friendship in
Sweden will be enough for the validation of more future scenarios. This same wish
goes to all my folks home and abroad, your support through the course of my studies
will never be forgotten.
This space cannot be enough to appreciate the help, support and prayers of my parents
and siblings, but I pray God’s blessings to enable me reciprocate your outstanding
attention on me.
And to her, the very love of my life, what you gave me cannot be equated with words.
2
TABLE OF CONTENT
ABSTRACT .................................................................................................................. 1
ACKNOWLEDGEMENT ............................................................................................ 2
TABLE OF CONTENT ................................................................................................ 3
LIST OF FIGURES AND TABLES ............................................................................. 4
1.1 INTRODUCTION................................................................................................... 5
1.1.1 STUDY OUTLINE .................................................................................. 6
1.1.2 STUDY OBJECTIVES ............................................................................ 6
1.2 BACKGROUND AND STATE OF RESEARCH ................................................. 6
1.2.1 SOURCES AND CYCLING OF CHLORINE IN A FOREST
ECOSYSTEM ................................................................................................... 6
1.2.2 CHLORIDE IMBALANCES IN THE SOIL ........................................... 8
1.2.3 MICROBES AND CHLORIDE RETENTION ....................................... 8
2.1 METHOD .............................................................................................................. 10
2.1.1 EXPERIMENTAL DATA AND DESCRIPTION ................................ 10
2.1.2 SYSTEM DEFINITION ........................................................................ 10
2.1.3 MODEL CONCEPTUALIZATION: Assumption and Calibration ....... 11
2.2 HYDROLOGY...................................................................................................... 12
2.2.1 Variables, Flows, Parameters and Parameterisation of the Modified HBV
Model .............................................................................................................. 13
2.3 BIOGEOCHEMICAL MODEL............................................................................ 15
2.3.1 Transformation ....................................................................................... 15
2.3.2 Microbial Biomass Assimilation (MBA) ............................................... 16
3.0 RESULTS.............................................................................................................. 18
3.1 HYDROLOGICAL SUB-MODEL ....................................................................... 18
3.2 BIOGEOCHEMICAL SUB-MODEL................................................................... 18
3.3 Biomass Carbon and Chloride Availability .......................................................... 28
3.4 Internal Dynamics of the Model............................................................................ 28
4.0 DISCUSSION ....................................................................................................... 29
4.1 Chloride Movement in Soils ................................................................................. 29
4.2 Organic matter and microbial activity................................................................... 30
4.3 Chloride loads and Microbial Retention ............................................................... 30
4.4 Microbes and the Retention-Release Pattern ........................................................ 30
4.5 Study Implications................................................................................................. 31
5.0 CONCLUSION ..................................................................................................... 32
REFERENCES ............................................................................................................ 33
APPENDIX…………………………………………………………………………..36
3
LIST OF FIGURES AND TABLES
Figure1: Conceptual diagram defining the system as a whole .................................... 10
Figure 2: Conceptual diagram of the modified HBV model ....................................... 13
Figure 3: Simulated accumulated outflow (mm) from the Lysimeters for a period of
122days........................................................................................................................ 18
Figure 4: Simulated Chloride concentration for Group 1 Lysimeters over a period of
122days. ...................................................................................................................... 21
Figure 5: Simulated Chloride concentration for Group 2 Lysimeters over a period of
122days........................................................................................................................ 23
Figure 6: Simulated Chloride concentration for Group 3 Lysimeters over a period of
122days. ...................................................................................................................... 25
Figure 7: Simulated Chloride concentration for Group 4 Lysimeters over a period of
122days. ...................................................................................................................... 27
Figure 8: Simulated Oxygen, Biomass Carbon and Modelled and Observed Chloride
concentration over a period of 122 days ..................................................................... 28
Table1: Different groups of the lysimeters ................................................................ 11
Table 2: Parameter values used for the hydrological sub-model ................................ 18
Table 3: Parameter values used for the biogeochemical model ................................. 19
Table 4: Comparison of the available modelled and observed chloride amount for
group 1 lysimeters ....................................................................................................... 20
Table 5: Comparison of the available modelled and observed chloride amount for
group 2 lysimeters ....................................................................................................... 22
Table 6: Comparison of the available modelled and observed chloride amount for
group 3 lysimeters ....................................................................................................... 24
Table 7: Comparison of the available modelled and observed chloride amount for
group 4 lysimeters ....................................................................................................... 26
Table 8: Comparison of the modelled available chloride amount and Biomass carbon
accumulated during the retention period. .................................................................... 28
4
1.1 INTRODUCTION
Chlorine is one of several elements that have been given little attention in early
studies of forest soils. There are less available knowledge as regards its interaction,
movement and different species in the soil environment. The negligence to study
chlorine and its behaviour in forest soils is borne out of the textbook conservative
hypothesis widely accepted by earlier researchers. It was believed that chlorine move
through the soil unreactive, and that, the exact amount deposited can be recovered as
leachates from the soil (Schlesinger, 1997). This idea informed the use of chlorine as
a mere conservative tracer of water and other solutes movement in the soil.
However, the last two decades have produced various researches into the study of
chlorine and its interactions in various ecosystems. The outcome of such published
works have led to the understanding of the existing species of chlorine; their
movement in the soil; their interaction with other components in the soil; possible
factors that influence their behaviour; and the overall consequence of their turnover in
various parts of the biosphere. These developments have led to the birth of a new nonconservative paradigm of chlorine and its different forms. The evolving paradigm has
informed the review of various old data and researches in order to validate the
descriptions presently offered about chlorine. One of such attempts was published by
Lovett et al (2005), where the researchers re-evaluated their findings about chlorine
over a period of almost four-decades in Hubbard Brook USA. Another attempt was a
review of old research findings about chlorine and its presence in different biological
life by Öberg (2002). Öberg fitted scattered research pieces (that were neglected
because of the conservative hypothesis) into a knowledge that was perfectly
consistent with the recently discovered non-conservative hypothesis. In her research
work, there was a special spotlight on the importance of chlorine in microbes and
their influence on the transformation of chlorine into other forms hitherto un-noticed.
Microorganisms are important components of all ecosystems. In forest ecosystems,
microorganism comprise mainly of fungi and bacteria. Hence, they are referred to as
soil microbes. Soil microbes play major roles in the sustenance and regeneration of
the environment they live in. The interaction of microbes with other soil components
such as major elements (e.g. carbon and nitrogen) is well documented (Wardle, 1992).
Their impact on the transformations of these major elements have also been
understood and under continuous research. Amongst various tools that have led to the
continuous understanding of microbial interaction with elements (such as carbon) has
been the use of numerical modelling. Modelling is a viable tool often used to integrate
scattered pieces of research knowledge (Jorgensen, 2001). It is usually very potent to
gain a better understanding of new areas of research that have gaps of knowledge to
bridge. Thus, an evolving area of research such as the biogeochemistry of chlorine
can be well understood with the use of modelling as a tool. This will help understand
the interaction between important factors influencing the transportation, movement
and availability of chlorine in forest soils.
Having referred the possible impacts of microbial activity on chlorine
biogeochemistry, it is imperative to employ modelling as a tool to understand the
influence of microorganism on the movement of chlorine in soils. This will enable a
proper description of the influence of microbial activity on chloride movement under
environmental factors that have been reported by Bastviken et al. (2006).
5
1.1.1 STUDY OUTLINE
Here, I started with a short synopsis on chlorine: its description and available forms. I
went ahead to explain the two chlorine forms of particular interest (i.e. inorganic
chlorine and organic chlorine) in this study, bringing to light their sources and cycling
in the environment. I went further to describe the current state of research in chlorine
biogeochemistry and the particular challenge that led to the objectives of this study.
1.1.2 STUDY OBJECTIVES
§
To evaluate the extent to which microbial activity explains the
biogeochemistry of chlorine.
§
To evaluate the extent to which microbial activity can account for the shift
from, chloride retention to chloride release.
§
To evaluate the extent to which microbial retention can control the availability
of chloride in soil pore water.
1.2 BACKGROUND AND STATE OF RESEARCH
The description, nomenclature and categorisation of the chlorine have witnessed
various dimensions through several decades of scientific research. Elemental chlorine
is a very reactive gas leached out to the environment after its volatilisation from the
hot magma that made up the earth’s crust (Johansson, 2000). Though a minor
constituent of igneous, metamorphic and sedimentary rocks, chlorine is available in
the ocean, with only hydrogen and oxygen having larger pools than it (Lovett. et al.
2005). Because of its reactivity, chlorine is rarely found in its elemental form in
natural environments, but rather as chloride - its ionic form. Chlorine with other
halogens such as, fluorine, bromine, iodine and astatine has a very high tendency to
form salts due to their high electronegativity and high bonding energies (Lovett. et al.
2005). Out of the Halogens, Chlorine is regarded as very important and the most
common after Fluorine (Johansson, 2000).
Chlorine exists in different forms and species in water, air and soil compartments. It
can exist in its more common form as inorganic chloride, as volatile or non-volatile
chlorinated compounds. Chlorine has been found to exist in these forms in different
part of the biosphere. Common examples of chlorine species are chloroform,
trichloroacetic acids, tetrachloroacetic acids, and chloromethane. (Laturnus, 2003;
Johansson, 2000; Svensson, 2006). Chlorine in its different forms, affects the health
and survival of various parts of the ecosystem, it threatens plant/animal life in soil and
water, and on a global scale destroys the ozone - the layer protecting the earth from
dangerous ultraviolet radiation (Laturnus, 2003).
1.2.1 SOURCES AND CYCLING OF CHLORINE IN A FOREST ECOSYSTEM
This section aims to bring forth the source and cycling of chlorine (with particular
attention on organic chlorine and inorganic chlorine), its interaction, presence and
occurrence in the vegetation and soil of a coniferous forest ecosystem.
6
CHLORIDE
Wind, water, weathering, precipitation, pH, ion exchange and soil organisms are
among the geochemical forces that characterise the cycling and transformation of
chloride ions (Öberg, 2003). Lovett et al. (2005) reviewed that, the movement of
chloride between the ocean and the atmosphere accounts largely for the global
chlorine cycle, with chloride from the ocean being the source of those found in both
terrestrial and fresh water ecosystems. Chloride is generated from the ocean as salt
aerosols through the continuous breaking of the ocean surface by waves. The ions in
the aerosols are transported by wind action and evaporation to the atmosphere; they
leave the atmosphere to the terrestrial environment either as wet or dry depositions
(Johansson, 2000). The amount of chloride ions deposited to an environment depends
largely on the depth of precipitation, the concentration of chloride in the precipitation
(Öberg, 1998), the distance of the environment from the ocean, topography and wind
direction (Öberg, 2003).
Different ages of research have reported various properties and behaviour of chloride
in the soil. Earlier researches favoured the conservativeness of chloride, while recent
studies have discovered chloride transformations in the soil. Earlier works reviewed
by Lovett et al. (2005) described chloride as only reactive in the atmosphere but
unreactive in the soil. It was assumed that chloride is poorly absorbed and do not
participate in the formation of secondary minerals. Most of the referred works were
measurements predominantly carried out to account for ion balances with little or no
study of other forms, properties and behaviour of chloride in the soil.
The conservative assumption about chloride informed its use as a tracer in the study
of most forest ecosystems. Johansson (2000) complemented this by reporting that the
conservative assumption made chloride together with other halides like bromide to be
used as mere tracers of water movement in hydrological modelling. They were used
primarily to describe the sources, transportation and transformation of other ions in
the soil. This was because the sources and sinks of chloride were understudied and
assumed negligible in the soil. On the contrary, Öberg (2002) reported recent studies
that confirmed chloride as not only participating in complex biogeochemical
reactions, but also transforms into organic forms in the soil.
The movement of chloride ions in the soil is associated with that of soil water.
Because of its solubility (Lovett et al, 2005) and negative charge, chloride ions move
at a very fast rate through the soil medium. This fast movement is aided by the repel
action between negatively charged chloride ion and solid structures (mostly clay and
organic matter) of the soil (Öberg, 2003)
ORGANIC CHLORINE
This specie of chlorine exists by bounding to carbon atoms (organic compounds),
with specific examples like fulvic and humic acids (Öberg 1998; Svensson 2006). The
main source of organo-chlorines in the environment was initially thought to be
anthropogenic - with suggested point sources from pulp and paper industry cum the
use of pesticides, herbicides and chlorinated solvents. However, recent studies have
confirmed natural sources of organo-chlorines. Johansson (2000), in her doctoral
thesis, reviewed recent researches that confirm detectable quantities of organochlorines in soil samples collected from remote areas all over the world. Results of the
researches revealed that organo-chlorines could be present as volatile, non-volatile,
high or low molecular weight compounds. The result also showed that organic matter
7
content of the analysed soils was related to the quantity of the discovered compounds.
In addition to this, Öberg (1998) also reported chloride amount in organic matter
being close to that of phosphorus but slightly less than sulphur and nitrogen.
Öberg (2003), revealed that in addition to the natural formation of organic chlorine in
soil, other major sources could be litter and through fall from trees as a result of
precipitation. Precipitation has been reported to wash down chloride particles stuck on
tree branches, needles and crowns.
The formation of organic chlorine in the soil has been strongly forwarded to be biotic
(Johansson et al., 2003) but Keppler et al. (2000) however suggested that their
formation could also be due to abiotic factors. A typical biotic mechanism is the
production of organic chlorine as a bye-product during the biodegradation of organic
matter. The biodegradation is a resultant effect of organic matter oxidation by reactive
chlorine e.g. Hypochlorite (HOCL). Specialised microbial enzymes were reported to
form this reactive chlorine in the presence of hydrogen peroxide and chloride ions
(Öberg, 2002).
1.2.2 CHLORIDE IMBALANCES IN THE SOIL
Chloride is the main source of chlorine that cycles the soil. As earlier explained, it is
majorly deposited to the soil with precipitation and through-fall (Öberg, 2003).
Chloride imbalance results when the amount of chloride deposited to the soil is not
completely recovered in form of leachate from the soil medium. This clearly
contradicts the original knowledge of chloride behaviour, hence focusing many
research works on the factors that could be responsible for chloride retention in soil.
Amongst major factors that have been identified to cause chloride imbalance in forest
soil are: vegetative uptake (Likens, 1995), ion exchange (Viers et al., 2001),
heterogeneous chloride movement in soil pores, evaporation and microbial uptake.
Among all these factors, Bastviken et al. (2007) reported microbial uptake as a major
and significant cause of chloride retention over long and short time periods. Thus, in
order to establish the relationship between chloride retention and microbial activity; it
is important to explain the processes and environmental factors that characterize the
existence of the microbes in forest soils.
1.2.3 MICROBES AND CHLORIDE RETENTION
Recent researches have shown that soil microbes could retain chloride through shortterm uptake for metabolism and long-term chlorination of soil organic matter.
Bastviken et al. (2007) showed that soil microbes could retain as much as 24 and 4
percent of the initially available chloride in soil water on short and long terms
respectively. Important factors that have been identified to affect the retention of
chloride by soil microbes are temperature, available organic matter, and oxygen
(Bastviken et al., 2006). The availability of easily digestible organic matter fractions
will increase microbial activity and hence an increase in chloride
assimilation/retention. Due to the fact that most microbial activities are enzymatic,
temperature optimum for enzymatic activities will increase microbial retention.
Microbes have also been reported to retain more chloride in oxic (i.e. abundant
8
oxygen) than anoxic (reduced/no oxygen) soil conditions (Thomsen, 2006). Bastviken
et al. (2006) also reported that microbial activities could decrease with a reduction in
the amount of oxygen available to oxidise refractory organic materials, when the
easily available ones have been depleted.
Bastviken et al., (2006) investigated the effect of water residence and nitrogen and
chloride loads on chloride retention and release in a forest soil. The retention noticed
was suggested to be more of a microbially induced process than any other means of
retention, considering the conditions under which the experiment was conducted.
However, the chloride retention rates in terms of microbial interaction with those
factors were not documented. Hence, it is imperative to quantify how much microbial
activity (with its limiting conditions like oxygen and organic matter) could explain
chloride retention under different water and chloride conditions. In a similar research
by Bastviken et al. (2007), to quantify microbial retention in a forest soil, it was
established that an increase in microbial biomass carbon in the orders of 10 percent
could reduce chloride in soil water by 25 percent, but the effect of this retention rate
in relation to fluctuations in environmental conditions was not elaborated.
9
2.1 METHOD
2.1.1 EXPERIMENTAL DATA AND DESCRIPTION
The data used for the model was obtained from a lysimeter experiment (Bastviken et
al., 2006) conducted on soil from the Stubbetorp catchment (58°, 44’ N, 16°, 21’ E) in
southeast Sweden. The catchment is a 0.87km2 coniferous forest predominantly
consisting of Norwegian spruce and pine trees. Undisturbed soil cores (of 15cm depth
and 80cm2 cross-sectional area) were collected and used to set-up a lysimeter
experiment according to Rodhstedt et al. (2003) specifications. They were incubated
in a climate chamber for 127 days at a temperature of 10oC and humidity of
approximately 90%. The Lysimeters were treated with three factors (chloride,
nitrogen and water) relative to the objective of the experiment and the factors were
made to two levels of high and low amounts. The experimental design resulted into
eight combinations and at three replicates each; there were 24 lysimeters in all. The
lysimeters were treated twice a week with artificial rain containing ion SO42-, Ca2+,
Mg2+, Na+, K+ and H+ corresponding to what obtains at the Stubbetorp catchment.
Percolation were measured weekly for the duration of the experiment.
2.1.2 SYSTEM DEFINITION
The natural soil environment represents the general system to be modelled. It has a
general outer boundary that describes inlets and outlets of driving variables
determining the condition of state variables. The system has two distinct internal
mechanisms (Fig.1) (Hydrology and Biogeochemistry), which are translated into
different models with their respective driving and parametric values.
Precipitation with
Chloride
Chloride
Precipitation
Hydrology
Boundaries
Biogeochemistry
Percolation
Chloride Outflow
LEACHATE
Figure1: Conceptual diagram defining the system as a whole
The precipitation that enters the soil system has a certain concentration of chloride.
Precipitation and chloride witness different dynamics that are regulated by hydrology
10
and biogeochemistry respectively. These two-subsystems are interconnected to
properly describe retention of chloride in soils. The above represents a simplification
of chloride transformation and the details covered in the model are as accurate as: the
state of research in biogeochemistry of chlorine and data availability.
2.1.3 MODEL CONCEPTUALIZATION: Assumption and Calibration
The two internal mechanisms of the system are made into a single-larger model (The
Hydrochemical Model). The hydrological sub-model is a modification of the HBV
model (Bergström et al., 1985) while the biogeochemical model was built to achieve
the objective of this study. The biogeochemical model represents a simplified system
with the inclusion of vital components influencing the transformation of chlorine
primarily by soil microbial community. This is in particular reference to the
justification offered as background to this study.
The main assumption of the model is that, the effect of nitrogen is not important on
the retention of chloride in the soil. This assumption is informed by a report by
Bastviken et al. (2006) – the particular research effort that motivated this present
study. With this assumption, the lysimeters are grouped as shown in Table 1.
Table1: Different groups of the lysimeters.
Precipitation
Chloride
Load
Lysimeter
replicates
LOW
LOW
1a, 1b, 1c
GROUP
1
2a, 2b, 2c
HIGH
2
4a, 4b, 4c
HIGH
LOW
3
5a, 5b, 5c
6a, 6b, 6c
HIGH
4
3a, 3b, 3c
7a, 7b, 7c
8a, 8b, 8c
One lysimeter from each group is calibrated and its parameter values subsequently
used to run input data for the other lysimeter replicates in the group. For example, in
group 1 Lysimeter 1(a) will be calibrated and its parameter values used to run
lysimeters 1(b) to 2(c). This will represent the general calibration procedure for the
groups unless otherwise stated. The general idea behind this assumption is to fulfil the
set aims and objective for this study.
11
The model’s qualitative performance will be tested through a sensitivity analysis and
the evaluation of model efficiency (R2) according to Nash and Sutcliffe (1970). The
efficiency ranges between a perfect fit value of 1 and -∞. If the model efficiency is
lower than zero, then, the mean of the observed time-series will be a better predictor
than the model (Krause et al., 2005). Important model outputs will also be compared
with the observed values to evaluate the quantitative performance of the model. The
R2 are calculated as:
n
∑ (Qcom (i) − Qobs (i))
2
R 2 = 1− i =1 n
( −∞ < R 2 ≤ 1)
∑ (Qobs (i) − Qobs ) 2
i =1
Q com: Modelled value
Q obs: Observed value
2.2 HYDROLOGY
The HBV Model
The original HBV model has been in use for more than thirty years (Lindström et
al.1997). It has undergone series of modification informed by the objective of
accurately estimating run-off conditions in catchments of varying sizes across
different regions of the world. The HBV-model is a semi-distributed conceptual
model that continuously calculate run-off for hydrological forecasting. It uses subbasins as the primary units and takes into account characteristic conditions (such as
land-use, area, and elevation) of the sub-basin for proper calculation of run-off
conditions over diverse land areas. It is divided into four sub-routines of snow
accumulation and melt; soil moisture accounting procedure; routines for run-off
generation; and simple routing procedure. Each of these routines has specific
parameters that define the characteristics of the sub-routine. The parameters are
calibrated accordingly during simulation procedures. Input data for the model are
precipitation, air temperature and estimates of potential evapotranspiration. The
model is usually run on daily time-step, but higher resolutions can be achieved
(Lindström et al.1997).
Use and Modification of the HBV Model
The HBV model has been adapted for numerous hydrological and hydrochemical
studies on various scales and dimensions. Andersson et al. (2005) adapted it for use in
estimating nitrogen and phosphorus flow in a catchment, Tonderski et al. (2005) used
it to estimate phosphorus retention in soils while Sanden (1991) also adapted it to
monitor metal transport in an old-mining area.
For this study, the original HBV model was simplified and adapted for our purpose
using the STELLA® software (HPS, Hannover, USA). The software has been used for
numerical modelling of various ecosystem studies, though, it has reduced capacity for
large data set, but it proved sufficient for this task. The model was simplified to
having one sub-routine. i.e. the soil moisture accounting procedure (see Fig 2). The
aim of this model is to simulate three hydrological inputs needed to estimate chloride
12
concentration in the lysimeters. The hydrological inputs needed in the biogeochemical
sub-model are:
1. Soil moisture balance: This is needed to quantify the chloride amount or
concentration in the soil of each lysimeters.
2. Percolation/run-off: This is also important for estimating the chloride amount
or concentration in the outflows/leachates from the lysimeters.
3. Actual evapotranspiration
In order to simulate the above, the model is used to primarily simulate
percolation/run-off for the soil lysimeters. Once, the modelled percolation is
consistent with observed outflow from the experiment, we can be sure that other
hydrological inputs needed to estimate chloride concentration in the soil and leachates
were accurate.
.
Precipitation
Actual Evapotranspiration
SOIL
Percolation/Run-off
Accumulated
outflow
Figure 2: Conceptual diagram of the modified HBV model.
2.2.1 Variables, Flows, Parameters and Parameterisation of the Modified HBV
Model
§
Precipitation: This is the main driving variable of the model. In the lysimeter
experiment, it represents the artificial rain treatment at two levels of high and low
as explained above i.e 1449mm and 483mm per annum, respectively. These
amounts correspond to the precipitation on the west and east coasts of southern
Sweden. The lysimeters were treated twice a week resulting in 38 data points, but
the data were adjusted to the length of the experiment (127 days) by allotting zero
values for days of the week without precipitation. This was done to generate a
13
continuous data set, which was essential for the numerical method used for the
simulations.
In order to estimate the gravimetric equivalent of the precipitation treatments
(1449mm and 483mm per annum), the values were measured in grams with the
assumption of 1 g ml-1 as density of water. The resulting millilitre (the equivalent
of grams) value was converted back to precipitation depth in millimetres (mm)
with respect to the cross-sectional area of the lysimeters (80 cm2).
122 observed data points (i.e. the duration of the experiment excluding the first
four days) were used to run the model. This was so done because the water
conditions for the first four days in the lysimeters were not stable for use by the
numerical method in STELLA®. The first amount of water added to the lysimeter
to achieve field capacity was too large.
Initial Moisture Content: This is an initial state parameter estimated for all
lysimeters. At the start of the experiment, the following were measured in grams:
A: weight of the lysimeter,
B: weight of the lysimeter with wet soil.
The wet weight of the soil was estimated by subtracting A from B, and then the
estimated dry weight (which was measured after the termination of the
experiment) was subtracted from the calculated wet weight to get the initial
moisture content.
§
The above estimate was primarily used as a guide to set the range for the different
initial moisture conditions in the lysimeters. It differed from the one used in the
model because of the wide differences noticed in the initial moisture conditions in
each of the lysimeters. These differences were reflected in the different dry
weights measured for all the lysimeters. The choice of a single calibrated moisture
value was to set the same initial soil conditions for the lysimeters.
§
Field Capacity (FC): This is also an initial state parameter used to regulate
percolation and evapotranspiration in the model. It was estimated as equal to the
initial moisture content because excess water was drained from the lysimeters at
the start of the experiment The same calibrated value was used for all the
lysimeters (irrespective of the treatments) because the same hydrological
conditions were assumed for them all.
§
Soil Curve Parameter (BETA): This model parameter was also set for all the
lysimeters irrespective of their treatments. It was used to define the distribution
of soil particles in order to regulate percolation in terms of the soil moisture.
§
Potential Evapotranspiration (Evapo): This was also calibrated and validated
for use in all the lysimeters. The unit is also in mm. It is one of the variables
used to regulate the actual evapotranspiration for all the lysimeters.
§
Alfa: The same Alfa values were set for all the lysimeters irrespective of their
treatments. It is a model parameter used to regulate the rate of
Evapotranspiration from the model.
The Model simulated three OUTPUT VARIABLES: Actual evapotranspiration, soil
moisture balance and percolation. Only two of these - soil moisture balance and
14
percolation were used as hydrological inputs in the biogeochemical model. All these
have units in mm.
2.3 BIOGEOCHEMICAL MODEL
As earlier justified in the background for this study, the soil microbial community will
represent the main agent of chloride transformation in the biogeochemical model.
Once again, the main aim for building this model is to simulate chloride outflows
using the same calibrated values for all the lysimeters irrespective of the different
chloride and water loads. In order to achieve this, the biogeochemical model was
further divided into two sub-models: transformation and microbial biomass
assimilation.
2.3.1 Transformation
This describes chloride transformation in the lysimeters in terms of mobilisation and
immobilisation. The former is synonymous to chloride release, while the latter is the
same as chloride retention. With the assumption that the transformation of chloride in
the lysimeters is primarily due to microbial activity, the model was conceptualised
such that, chloride retention and release are driven by the second sub-model microbial biomass assimilation.
2.3.1.1 Variables, Flows, Parameters and Parameterisation of Chloride
Transformation Model
Chloride with precipitation: This is the main driving variable for the transformation
model. Chloride (given as mg Cl-1) enters the model with precipitation and these
represents the two treatment levels of chloride as earlier explained. The experimental
treatments are equivalent to 1449 and 4346 mg m-2 yr-1 of chloride which represents
the moderate load on the Stubbetorp catchment and loads along the west coast of
Sweden respectively. Since the chloride treatment came with precipitation, it also
corresponds to 38 data points (at two treatments per week), but the data were adjusted
to the length of the experiment (127 days) by allotting zero values for days of the
week without precipitation.
With specific reference to the number of data points (122) used to run the
hydrological sub-model (which serves as an input into this sub-model) the first
four days of chloride input were also excluded to assure the required internal logic
for the model.
§
Initial chloride amount in the soil: This initial state parameter was estimated
from the data. It corresponds to the initial chloride amount measured prior to any
treatment plus the amount in the first artificial rain minus the first amount leached
from the lysimeters. This estimation was done to return lysimeters with the same
chloride treatment to the same initial chloride amount, owing to the fact that they
might have different initial chloride contents. This also served as a means of
accounting for the effect of the first chloride load on the initial conditions in the
lysimeters. The hydrological input (i.e. the moisture balance) was used here to
calculate the concentration of chloride in the soil and the outflow from the soil.
15
The soil moisture balance for the hydrological sub-model was converted back to
litres before use for uniformity.
The estimated amount from the above calculation differed from the ones used in
the model because of the excluded first four days from the data. However, the
estimates provided a range of initial values appropriate to start the calibration for
the different lysimeter treatments. The same initial chloride amounts were set for
lysimeters with specific reference to the model assumption and calibration
procedure earlier stated.
§
Mobilisation: This is one of the main processes that drive the objective of this
model. It is classified an input because it is an internal flow process that returns
the immobilised chloride in the microbes back to the soil. In theory, Öberg (1998)
reported that the death of soil microbes would result in a release of the chloride in
their biomass. Thus, this process is directly connected to the outflow from the
other sub-model - microbial biomass assimilation (see explanation of this submodel below), and the release of chloride from the microbes is proportional to the
chloride in their biomass.
§
Immobilisation: This represents the output process that retains chloride in the soil
through microbial accumulation. As justified above, Öberg (2003) and Bastviken
et al., (2007) reported that soil microbial community retains chloride on long and
short-term periods. In this model, the process is connected to an estimated
chloride amount in biomass and the amount of microbial biomass accumulated
with time.
§
Chloride Outflow: This is also an important output variable from the soil. Its
concentration is equivalent to the chloride concentration after the initial amount
has undergone temporal or permanent transformation in the soil.
§
Chloride in Biomass: This was calculated from reported (Luria, 1960) estimates
of chloride amounts in cell structures of most living organisms.
2.3.2 Microbial Biomass Assimilation (MBA)
This sub-model drives the transformation of chloride in soil. Both immobilisation and
mobilisation processes are directly related to the rate of biomass carbon accumulation.
In theory, microbes have been reported to use chloride as transport electrolyte and for
maintaining osmotic balance (Öberg, 1998). Thus, with the availability of vital
resources such as oxygen and available organic matter, the assimilation of chloride
will continue with biomass growth. For this particular study, oxygen alone was
considered the main resources limiting the accumulation of microbial biomass for the
following reasons:
§
§
There was no data available for different organic matter fractions that could be
used to limit the growth of microbes with time. Data on different fraction of
organic matter is needed because microbes reduce in biomass growth when the
easily degradable organic matter fractions of the soil have been exhausted.
Oxygen has been reported to limit the rate of chloride retention (Thomsen,
2006) and metabolic activity of the microbes. In addition, the reported rate
16
(Raubuch, 1999) of oxygen consumption by microbes in coniferous soil will
serve as an appropriate literature guide for the calibration of oxygen
consumption in our model.
2.3.2.1 Variables, Flows, Parameters and Parameterisation of MBA sub-model
Initial Microbial Biomass Carbon: This is an initial state parameter for the MBA
sub-model. The initial biomass content reported (Bauhus et al., 1999; Raubuch and
Beese1995,2005; Friedel et al., 2006; Klose et al. 2004) for coniferous soils informed
the starting value for its calibration. The unit of the biomass content was converted to
milligram for consistency with other mass units in the biogeochemical model. For the
model, the same initial biomass carbon content of 100 mg (which is equivalent to 12.5
g m-2) was used for all lysimeters irrespective of the different treatments.
§
Microbial Growth: This is the main inflow into the MBA. It is an increase in
microbial biomass C with time at a growth rate calibrated for all the lysimeters.
Microbial accumulation was regulated with oxygen through an oxygen sub-model
built primarily for this. The objective of building the oxygen sub-model was to
regulate microbial growth with time and resource depletion as noticed in the
experiment. Though, the primary limitation here was that, the oxygen content of
the soil at the start of the experiment could not be estimated because of lack of
data.
§
Initial Oxygen content: The same initial oxygen content was set for all
lysimeters with specific reference to the general model assumption and calibration
procedure earlier stated. This will elucidate the synergetic effect of different
chloride and water loads on initial oxygen contents. This will also ensure same
initial oxygen states necessary to evaluate the efficiency of the model on these sets
of lysimeters. The oxygen depletion was regulated by a metabolic quotient (rate of
oxygen depleted per time per biomass C which was started with a range as
reported by Raubuch and Beese 1999.
§
Microbial Biomass Increase and Decrease rates: These two model parameters
were calibrated for use in the model. They were set for lysimeters with the same
chloride and water treatments in order to ensure the same initial conditions
necessary to compare the model efficiency on these lysimeters
§
Metabolic Quotient: The metabolic quotient was calibrated for all the lysimeters
irrespective of their different treatments.
17
3.0 RESULTS
3.1 HYDROLOGICAL SUB-MODEL
The modelled accumulated outflow from all the lysimeters was in good agreement
with the observed accumulated outflow using the parameter values shown in Table 2.
Figure 3 represents the general picture of the simulations for all the lysimeters. The
general pattern was confirmed when a sensitivity procedure was performed for the
model by altering the field capacity and initial moisture content to extreme values,
there was no difference in the accumulated outflow pattern for all the lysimeters after
the sensitivity test. The lysimeters returned an R2 value of approximately 0.99
calculated according to Nash and Sutcliffe (1970). This indicates that the model
accounted well for the hydrological conditions in all the lysimeters.
Table 2: Parameter values used for the hydrological sub-model
All lysimeters
Soil curve parameter Beta
20
Field Capacity (mm)
83.5
Initial Soil Moisture (mm)
83.5
Potential Evapotranspiration (mm)
0.38
Alfa
2
accumulated outflows (mm)
140
R2 = 0.99
120
100
80
o b s e rv e d
60
m o d e lle d
40
20
0
0
50
100
150
D ays
Figure 3: Simulated accumulated outflow (mm) from the Lysimeters for a period of 122days.
3.2 BIOGEOCHEMICAL SUB-MODEL
Table 3 shows the parameter values used to simulate chloride concentration and
amounts over a period of 122 days in the lysimeters. The parameter values were
determined with specific reference to the model conceptualization earlier explained
and shown in Table 1. The lysimeters returned different parameter values for the same
starting biomass carbon content (100 mg) and metabolic quotient (0.045 mg O2 d-1
18
mg-1 C). The biomass increase rate ranged from 0.06 to 0.14 represented by group 3
and group 2 lysimeters respectively. The biomass decrease rate ranged from 0.009 to
0.02, for group 2 and 1 together with 3 lysimeters respectively. The model produced
the best fit with two initial oxygen conditions of 500 and 650 milligrams. Groups 1
and 3 had 500 mg while groups 2 and 4 had 650 milligrams of oxygen.
In order to evaluate the implications of the parameter values used, a sensitivity
analysis was carried out on the model by doubling the starting biomass carbon
content. The test returned a model with a lower R2 value, though the differences in the
values did not affect the interpretation of the efficiency. i.e. the reduced values were
still in the same interpretation range with the new values. Specific examples were
lysimeters 7b and 8a where the efficiencies were reduced from 0.603 to 0.4 and 0.644
to 0.46 respectively.
Table 3: Parameter values used for the biogeochemical model
Precipitation
Chloride
Load
*Lysimeter
treatment
LOW
LOW
1
Group
1
2
2
HIGH
3
4
LOW
Initial
Chloride
(mg)
Initial
O2
(mg)
0.1
0.02
1.3
500
0.14
0.009
5
650
0.06
0.02
0.6
500
0.1
0.01
1.0
650
5
6
HIGH
Biomass
Decrease
Rate
3
4
HIGH
Biomass
Increase
Rate
7
8
* The simulation was done for the three replicates of each lysimeter treatment.
Figures 4-7 describe the general picture of the simulation and observed chloride
concentration for all groups of lysimeters while Tables 4-7 describes a comparison of
the modelled and observed chloride amount. Chloride amounts in the tables are
accumulated amount of chloride available in the soil during the retention and release
period of the simulation. This estimate was done to evaluate the quantitative
performance of the model. The retention represents the period when the chloride
concentration in the leachate decreased temporarily before a subsequent increase
(period of release) as observed in almost all the lysimeters. The lysimeters have
different times of shift from retention to release, and this is shown as the retention
period in days on the tables. This estimation is done to compare the simulated output
with the observed at both periods. The model R2 values for the different lysimeters
were calculated according to the plotted figures using the estimate of Nash and
Sutcliffe (1970). The R2 estimate was done to evaluate the qualitative performance of
the model. The figures describe the different behavioural patterns of the lysimeters
that resulted in values (in amounts) as shown on the tables.
19
The pattern of the observed and modelled concentration of chloride for group 1
lysimeters (i.e. those under low precipitation and low chloride load) is presented as
shown in Figure 4. For all the lysimeter treatments, the observed retention period
ranged between 24-38 days (Table 4). The model (as shown in Table 4) for this group
of lysimeters had chloride retained for 24 days with an available amount of 0.23 and
2.07 mg during the retention and release periods respectively. However, the observed
available chloride amount during retention ranged from no value during retention in
lysimeter 2c, to a maximum value of 0.47 mg in lysimeter 2b. The observed available
amount during chloride release for all the lysimeters also ranged between 1.64 mg in
lysimeter 2b to a maximum value of 2.96 mg in lysimeter 1c. This range did not
include the value for 2c because of the exceptional pattern observed in this replicate.
Model efficiency (R2) values ranged from as low as -2.57 for lysimeter 2c to a very
strong value of 0.826 for lysimeter 1c.
It can be seen from Figure 4 that the model performed considerably better for all the
treatment replicates of lysimeter 1. This was however different for lysimeter 2
replicates, where only 2a was fairly described with an efficiency of 0.668. Lysimeter
2c was exceptionally low most probably because of the observed behaviour of no
retention during the course of the experiment. For all the lysimeters, it can also be
discerned that the model accounted less for the observed values during the release
period than the retention period. The model did not account for the noticed drops in
chloride concentration during the release of chloride (see Figure 4).
Table 4: Comparison of the available modelled and observed chloride amount for group 1 lysimeters
Lysimeter Observations
Model
1a
1b
1c
2a
2b*
2c
during chloride
retention (mg)
0.23
0.25
0.12
0.12
0.10
0.47
No distinct
retention
Available amount
during chloride release
(mg)
2.07
2.26
2.27
2.96
2.16
1.64
0
Retention period (days)
24
31
24
24
31
38
0
Available amount
* Values are to be compared with an alternative model calculation of 0.13 mg as the available amount
during chloride retention. This is because an initial observed value was specifically missing for this
lysimeter at the start of the simulation.
20
7
Chloride Conc. (mg/L)
Chloride conc (mg/L)
6
5
Lysimeter 1a
4
3
R2 = 0.752
2
1
Lysimeter 2a
6
5
4
R2 = 0.668
3
2
1
0
0
0
0
50 DAYS 100
50 DAYS 100
150
150
7
Chloride Conc. (mg/L)
8
Chloride conc (mg/L)
7
Lysimeter 1b
6
5
4
3
2
R = 0.644
2
5
R2 = 0.012
Lysimeter 2b
4
3
2
1
1
0
0
0
50 DAYS 100
0
150
50
100
150
DAYS
12
Ch
lorid e C onc.
(m g/L
)
Chloride
Conc.
(mg/L)
7
Chloride Conc. (mg/L)
6
6
Lysimeter 1c
5
4
3
R2 = 0.826
2
1
0
12
10
11
10
98
8
76
6
5
4
4
3
22
1
00
Lysimeter 2c
Lysimeter 2c
R2 = - 2.57
00
0
50
100
150
5050
100100
150 150
DAYS
DAYS
DAYS
Figure 4: Simulated chloride concentration for Group 1 Lysimeters over a period of 122days. Plotted
line represents the simulated concentration while the squares represent the observed.
21
Model behaviour and outputs in relation to the observed scenarios for lysimeters in
group 2 (i.e. those under low precipitation and high chloride treatment) are presented
in Figure 5 and Table 5. The model for this group of lysimeters retained chloride for
the first 24 days of the simulation period. The model simulated an available chloride
amount of 1.00 mg for the retention period and 5.91 mg during the release period. The
observed available chloride amount during retention ranges from the lowest value of
1.12 mg in 3a to the highest value of 1.97 mg in 3c. The observed available chloride
amount during release also ranged between 3.74 mg and 7.46 mg for all the
lysimeters. The lysimeters had two retention periods of 31 and 38 for lysimeter 3a and
all other lysimeters respectively. The model efficiency also ranged from as low as 1.127 in lysimeter 3c to a good performance of 0.924 in lysimeter 3a (Figure 5). The
model can be said to fairly represent the overall observed behaviour for this group of
lysimeters. The model accounted better for the observed pattern during the release
period for this group of lysimeters as compared with group 1’s model performance
during this period.
Lysimeter 3c can be said to record the least similarity with the model because of its
exceptional behavioural pattern during the release period. It can be seen that this
replicate showed some peculiar fluctuations during the release period. It had the least
available amount during chloride release for all the lysimeters in the group.
Table 5: Comparison of the available modelled and observed chloride amount for group 2 lysimeters
Lysimeter Observations
Model
3a*
3b
3c
4a
4b
4c*
Available amount
during chloride retention
(mg)
1.00
1.12
1.48
1.97
1.62
1.67
Available amount
during chloride release
(mg/l)
5.91
7.37
6.18
3.74
7.14
7.46
7.28
Retention period (days)
24
31
38
38
38
38
38
1.86
* Values are to be compared with an alternative model calculation of 0.58 mg as the available amount
during chloride retention. This is because an initial observed value was specifically missing for this
lysimeter at the start of the simulation.
22
14
14
Lysimeter 3a
Chloride Conc. (mg/L)
Chloride Conc. (mg/L)
12
10
8
6
R2 = 0.924
4
12
Lysimeter 4a
10
8
R2 = 0.831
6
4
2
2
0
0
0
0
50
DAYS 100
150
150
14
12
Chloride Conc. (mg/L)
Chloride Conc. (mg/L))
DAYS 100
16
14
Lysimeter 3b
10
8
6
R2 = 0.717
4
2
Lysimeter 4b
12
10
8
R2 = 0.722
6
4
2
0
0
0
50 DAYS 100
0
150
50 DAYS 100
150
14
18
14
Lysimeter 3c
Chloride Conc. (mg/L)
16
Chloride Conc. (mg/L)
50
R2 = -1.127
12
10
8
6
4
2
12
Lysimeter 4c
10
8
6
R2 = 0.682
4
2
0
0
0
50 DAYS
100
150
0
50 DAYS 100
150
Figure 5: Simulated chloride concentration for Group 2 Lysimeters over a period of 122days.
Plotted line represents the simulated concentration while the squares represent the observed.
23
The model description and simulation for group 3 lysimeters (i.e. lysimeter treatments
under high precipitation and low chloride input) is presented as shown in Table 6 and
Figure 6. The model for this group of lysimeters retained chloride for the first 24 days
of the simulation period. The model simulated an available chloride amount of 0.41
mg for the retention period and 3.17 mg during the release period. The observed
available chloride amount during retention ranges from the lowest value of 0.31 mg in
5b to the highest value of 1.52 mg in 6c. The observed available chloride amount
during release also ranged between 1.17 mg and 3.98 mg for all the lysimeters. The
observed retention days for the lysimeter treatments were 31, 38 and 52 days with 5a,
5c and 6a having a retention of 31 days, 5b and 6c retained chloride for 52 days while
6b retained for 38 days (see Table 6). The model efficiency also ranged from as low
as -1.901 in lysimeter 5b to 0.643 in lysimeter 5a (Figure 6).
Thus, it can be discerned that the model did not properly describe the observed
chloride behaviour for the group. From Figure 6, it can be seen that the model made a
general overestimation for most of the lysimeters. The model could not account well
for the low chloride outflows observed in the lysimeters especially during the release
period of the simulation. The model stopped the retention earlier and initiated release
earlier, faster and higher than what was observed in the lysimeters. The model for this
group of lysimeters performed less than the initially noticed fair performance of group
1’s model.
Table 6: Comparison of the available modelled and observed chloride amount for group 3 lysimeters
Lysimeter Observations
Model
5a
5b
5c
6a
6b
6c
during Chloride
retention (mg)
0.41
0.54
0.31
0.67
0.42
0.39
1.52
Available amount
during Chloride release
(mg)
3.17
3.98
1.75
3.50
2.88
2.82
1.17
Retention period (days)
24
31
52
31
31
38
52
Available amount
24
2.5
Chloride Conc. (mg/L)
Chloride Conc. (mg/L)
1.6
Chloride Conc. (mg/L)
1.8
Lysim eter 5a
Lysimeter 5a
2
1.4
1.2
1.5
1
0.8
1
R2 = 0.643
0.6
0.4
0.5
0.2
0
0
0
50
0
50
100
DAYS 100
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Lysimeter 6a
R2 = -0.379
0
150
50 DAYS 100
150
150
DAYS
1.8
Lysimeter 5b
1.4
Chloride Conc. (mg/L)
Chloride Conc. (mg/L)
1.6
1.2
1
0.8
0.6
0.4
R2 = -1.901
0.2
0
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
50
DAYS 100
150
Lysimeter 5c
R2 = 0.311
0
50 DAYS
100
150
Lysimeter 6b
R2 = 0.331
0
ChlorideConc.
Conc. (mg/L)
Chloride
(mg/L)
Chloride Conc. (mg/L)
0
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
55
4.8
4.6
4.5
4.4
4.2
44
3.8
3.6
3.5
3.4
3.2
33
2.8
2.6
2.5
2.4
2.2
22
1.8
1.6
1.4
1.5
1.2
11
0.8
0.6
0.4
0.5
0.2
0
0
50 DAYS 100
150
R2 = -0.614
Lysimeter
Lysimeter
6c 6c
0
0
50
100
150
50 DAYS 100
DAYS
150
Figure 6: Simulated chloride concentration for Group 3 Lysimeters over a period of 122days. Plotted
line represents the simulated concentration while the squares represent the observed.
25
Also presented in Figure 7 and Table 7 is the model outcome in comparison with the
observed patterns for lysimeters in group 4 (i.e. lysimeter treatments under high
precipitation and high chloride input). The model for this group of lysimeters retained
chloride for the first 24 days of the simulation period. The model simulated an
available chloride amount of 0.63 mg for the retention period and 8.03 mg during the
release period. The retention period of the model was similar to most of lysimeters as
all of them but lysimeter 7c also had 24 days for the temporal retention period of
chloride. The observed available chloride amount during retention ranges from the
lowest value of 0.36 mg in 8a to the highest value of 0.83 mg in 8c. The observed
available chloride amount during release also ranged between 6.68 mg and 8.97 mg
for all the lysimeters. The model efficiency also ranged from as low as -0.057 in
lysimeter 7c to an average performance of 0.644 in lysimeter 8a (Figure 7).
The model can be said to perform considerably well for this group in terms of having
the general pattern observed for most of the lysimeters. This can be asserted with the
accuracy level depicted in the retention day modelled for the lysimeters. However, the
performance of the model was reduced, because of its inability to account for the
irregular increase pattern and outlier values noticed during the release period of most
of the lysimeters. The outlier effect is mostly evident in lysimeters 7c, 7a and 8c.
Thus, the model did a slight overestimate for some of the lysimeters.
Table 7: Comparison of the available modelled and observed chloride amount for group 4 lysimeters
Lysimeter Observations
Model
7a
7b
7c
8a
8b
8c
Available amount
during chloride retention
(mg)
0.63
0.46
0.53
0.58
0.36
0.41
0.83
Available amount
during chloride release
(mg)
8.03
6.68
8.71
7.18
8.50
6.87
8.97
Retention period (days)
24
24
24
31
24
24
24
26
5
Lysimeter 7a
0
Chloride
Conc.
Chloride
Conc.(mg/L)
(mg/L)
Chloride Conc. (mg/L)
65.5
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
50 DAYS
100
54.5 Lysimeter 8a
4
43.5
Lysim eter 8a
3
2.5
3
2
1.5
21
0.5
10
R2 = 0.644
0
150
50
100
150
0
0
DAYS
50 DAYS 100
150
5
Lysimeter 7b
4
Chloride Conc. (mg/L)
Chloride Conc. (mg/L)
4.5
3.5
3
2.5
2
1.5
1
0.5
0
50
DAYS 100
150
Chloride Conc.
Conc. (mg/L)
Chloride
(mg/L)
55
4.5
44
3.5
33
2.5
22
1.5
11
0.5
00
Lysim eter 7c
Lysimeter 7c
R2 = -0.057
00
50
50
DAYS
100
100
150150
Lysimeter 8b
0
Chloride Conc. (mg/L)
0
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
50 DAYS 100
150
Lysimeter 8c
0
50 DAYS
100
150
DAYS
Figure 7: Simulated chloride concentration for Group 4 Lysimeters over a period of 122days. Plotted
line represents the simulated concentration while the squares represent the observed.
27
3.3 Biomass Carbon and Chloride Availability
Table 8 below represents the average modelled estimate of the available chloride
amount in the soil pore water during the active growth of the microbes. It is defined
by the retention period of 24 days as modelled for all the lysimeters. It is assumed that
they are retaining chloride due to their uptake for growth. As presented in the Table 8,
the maximum biomass carbon represents the modelled amount accumulated as at the
retention day (24) for all the lysimeter groups.
Table 8: Comparison of the modelled available chloride amount and biomass carbon accumulated during the
retention period.
Group 1
Group 2
Group 3
Group 4
Available chloride amount (mg)
0.02
0.10
0.04
0.06
Biomass Carbon
(mg)
420
1300
210
580
3.4 Internal Dynamics of the Model
With oxygen as the limiting factor for microbial growth, the model revealed some
internal dynamics of chloride transformation in terms of retention and release, oxygen
depletion, and the increase and decrease of the biomass carbon in soil microbes. The
dynamics showed in Figure 8 represents a description of the model behaviour for
almost all the lysimeters. The figure shows plotted oxygen and biomass carbon
amount in milligrams with modelled and observed chloride concentration (in mg/l) for
one of the lysimeters. The model described a drop in biomass carbon content of the
microbes at around the time the oxygen content dropped to zero. The initial period
(increase in biomass carbon with oxygen availability) also describes the retention of
chloride from the soil water while the subsequent period (decrease in biomass carbon
at zero oxygen level) describes the release of chloride back to the soil water.
Cl(mg/L)
120
113
106
99
92
0
85
0
78
1
71
100
64
2
57
200
50
3
43
300
36
4
29
400
22
5
15
500
8
6
1
O2
(mg)
600
D AYS
Figure 8: Simulated oxygen, biomass carbon and modelled and observed chloride concentration over a
period of 122 days
28
4.0 DISCUSSION
4.1 Chloride Movement in Soils
This is a first attempt to model the biogeochemistry of chlorine in soil. Thus, it
brought different challenges in terms of comparison with published result. The main
challenge is fulfilling the required details appropriate to build a biogeochemical
model with limited available information. For this study, the building of the model
involved careful selection of internal and external factors reported to have influenced
the biogeochemistry of chlorine in forest soil. It also entailed the careful selection of
reported parametric values that are relevant to the ecosystem of coniferous soils.
In the face of the aforementioned challenges, the model outcome was able to
complement and provide better understanding of the existing paradigms in chlorine
biogeochemistry. Simulation patterns showed in Figures 4-7 for all the lysimeters
clearly described the reported (Öberg and Sanden 2005; Öberg et al., 2005) nonconservative hypothesis. It revealed an initial retention of the deposited chloride and a
subsequent release into the soil under different water and chloride loads. In general,
the model made a considerable description of the reported chloride movement in the
soil, even with the underlying assumption.
The model could be considered adequate to support the non-conservative hypothesis
because; the important parametric values that were assumed to regulate the
transformation of chloride (such as initial biomass carbon, metabolic quotient, and
biomass increase and decrease rate) were consistent with reported values for forest
soils. The modelled biomass carbon content ranged from 100 to 922.64 mg (which is
equivalent to 12.5 -115.33 g m-2). This range was consistent with the reported biomass
content (12 - 422 g m-2) of forest soils as reported by Bauhus and Khana (1999),
Raubuch and Beese (1995; 2005) and Friedel et al. (2006). The metabolic quotient
modelled for all the lysimeter (0.045 mg O2 d-1 mg-1 C) was also in the same order of
magnitude as reported by Raubuch and Beese (1999).
Though, the model had different strength of efficiencies and outcome comparison
with the observed (Tables 4-7), Figures 4-7 still defines it appropriate because of its
ability to repeat the same trend of scenarios for all the lysimeters irrespective of the
interaction of the different water and chloride treatment under varying soil conditions.
The biogeochemical part of the model described the retention of chlorine in terms of
mobilisation and immobilisation through microbial activity alone with the inclusion of
oxygen as the main limiting factor for microbial growth. These are parts of the
model’s simplifications that may have limited its performance. The present
consideration of microbial activity as the only factor responsible for chloride retention
could be part of the reasons why the model had low efficiencies for lysimeters under
high water loads (short residence time) compared with those under low water loads
(long residence time). It can be hypothesised that high water residence in group 1 and
2 lysimeters encouraged both chemical and biological activities of the soil – a
situation that will enhance the surface reaction of soil particles and the microbes.
Thus, these conditions could be speculated to have brought the best performance from
the model for this group, since the retention of chloride was primarily assumed
microbial.
29
For lysimeters (group 3 and 4) under short water residence time (situations that
witnessed low model efficiency), it could be hypothesised that microbial
transformation was not completely favoured, because there is a reduced reaction as a
result of the short water residence. Hence, a microbial driven model may not
completely describe chloride transformation in the lysimeters under this condition. It
could be speculated here that, other factors not incorporated in the model may have
predominate under unfavourable conditions for biological reactions.
4.2 Organic matter and microbial activity
Another possible reason that may have affected the model performance under varying
water conditions is that, oxygen alone may not have completely limited microbial
activity under different water residence time. The performance of the model could
have been enhanced for both water residence periods if organic matter had been
included in the model. Availability of different fractions of organic matter was
forwarded by Bastviken et al. (2006) to influence the activity of soil microbes at
different levels of oxygen availability, most especially during the immobilisation of
chloride ions. The oxygen - organic matter - microbe interaction could have led to a
better description of chloride retention by microbial activity alone. For this study,
there was no data available for the different fractions of organic matter in the
lysimeters. Thus, the reported efficiencies and amounts of modelling microbial
transformation of chloride in the lysimeters may have been underestimated.
4.3 Chloride loads and Microbial Retention
Result of this study also revealed that, the model had better performance under high
chloride loads compared with low chloride loads. In earlier studies (Bastviken et al.
2006, Johansson et al. 2003) it was reported that higher chloride loads could influence
the net-retention of chloride ions, however, there was no report suggesting that this
could be largely described by microbial action. Figures 4-7 showed a better model
efficiency for lysimeters treated with high chloride loads compared to those treated
with low chloride loads under the same water treatment. This suggests that an increase
in chloride deposition could generally enhance the activity of soil microbes and their
consequent ability to retain chloride.
4.4 Microbes and the Retention-Release Pattern
One of the characteristic behaviour of the non-conservative hypothesis is the sudden
shift from net chloride retention to net release as showed in Figures 4-7. Bastviken et
al. 2006 noticed this behaviour and they speculated possible soil reactions that could
be responsible. They suggested microbial activity amongst other factors that maybe
responsible. The model described the net retention release pattern for almost all the
lysimeters and it described the shift at around the same period (see Tables 4-7) as
reported by Bastviken et al. 2006. Thus, it can be affirmed that the limitation of
microbial activity by oxygen or other conditions is an important factor that could be
responsible for the shift noticed and described for the non-conservative behaviour of
chloride in soils. The Figures (4-7) also confirms the substantial impact of microbial
activity on chloride retention amongst other speculations.
30
4.5 Study Implications
The outcome of this study can be extrapolated to catchment scale depending on the
availability of data that will enable a comparison with the experimental set-up of this
study. The models, though, with varying efficiencies, simulated a range of 0.02 – 0.10
mg as the average amount of chloride available per day during the active period of the
microbes. Thus, the simulated range mentioned here could represent the amount of
chloride that will be available in the soil pore at different water and chloride
deposition on a catchment scale. As modelled for all the lysimeter groups, the
retention period of the model was 24 days, and at this time, the maximum biomass
carbon accumulated were 210, 420, 580 and 1300 mg for group 3, 1, 4 and 2 models
respectively.
It can be discerned that, seasons with low precipitation in the order of 483 mm (i.e.
low water treatment for this study) and high chloride deposition around 4346 mg m-2
y-1 (i.e. High chloride treatment for this study) will likely favour maximum biomass
carbon accumulation with an approximate availability of 0.10 mg Cl d-1. It can also be
deduced that, seasons with low precipitation depth of around 483 mm (i.e. low water
treatment for this study) and low chloride deposition of around 1449 mg m-2 y-1 (i.e.
low chloride treatment for this study) will less likely favour biomass carbon
accumulation and could make 0.02 mg Cl- d-1 available in the soil.
With specific reference to seasons, varying biomass carbon amount has been reported
for temperate forest soils (e.g. Diaz et al. 1995; Raubuch and Joergensen 2002). It has
been shown that, the seasons favour microbial biomass abundance in this order:
Spring > winter >summer >autumn. Thus, the availability of chloride in the soil pore
water in relation to biomass activity will depend on the interaction between the
periods of resource (such as oxygen and available organic matter) availability;
transition between the seasons; and the predominant chloride and water quantities.
This interaction will help evaluate the exact amount of chloride that will be available
in the soil under these different conditions.
31
5.0 CONCLUSION
Modelling microbial retention of chlorine has revealed the importance and impact of
microbial activity on chloride transport in forest soils. It has been shown that,
microbes could be responsible for the short-term shifts from net chloride retention to
net chloride release that is characteristic of the non-conservative hypothesis of
chlorine movement. This indicates that microbes could be a possible source or sink of
chloride in forest soils. This result further strengthens the non- conservative
hypothesis of chlorine biogeochemistry. Hence, the use of chloride as a conservative
tracer in hydrological studies should be re-considered.
The study also revealed that modelling the impact of microbial activity on chloride
retention under different water and chloride loads could be underestimated.
Underestimation of their impact is imminent if the microbes are limited by oxygen
availability alone. The microbes could better describe chlorine transformation if other
factors limiting their activities (like organic matter fractions) are considered.
However, it was discerned that, oxygen could be an enough factor to cause short-term
shifts in chlorine transformation as proved by the result of this study.
Thus, to properly account for the transformation of chlorine in forest soils, it is
imperative to consider more factors that have been speculated to influence the
biogeochemistry of chlorine. The present study is a novel attempt, hence the
introduction of more factors into a chlorine hydrochemical model, could proof
sufficient to budget and describe the movement of chlorine on a catchment scale.
32
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35
APPENDIX: The STELLA code of the Hydrochemical model.
Biomass_CC(t) = Biomass_CC(t - dt) + (inflow - outflow) * dt
INIT Biomass_CC = 10
INFLOWS:
inflow = rel_oxygen*Biomass_CC*rate_or_Bincrease
OUTFLOWS:
outflow = 1*rate_of_Bdecrease*Biomass_CC
Cl_in_soil(t) = Cl_in_soil(t - dt) + (Cl_inflow + mobilisation - Cl_aoumt_outflow immobilisation) * dt
INIT Cl_in_soil = 23
INFLOWS:
Cl_inflow = Cl_amout_in_precipitation_dep
mobilisation = outflow*Cl_in_biomass
OUTFLOWS:
Cl_aoumt_outflow = Conc_Cl_outflow*P_conversion_to_litre
immobilisation = inflow*Cl_in_biomass
Cl_out_acc(t) = Cl_out_acc(t - dt) + (Cl_aoumt_outflow) * dt
INIT Cl_out_acc = 0
INFLOWS:
Cl_aoumt_outflow = Conc_Cl_outflow*P_conversion_to_litre
Cumulated_outflow(t) = Cumulated_outflow(t - dt) + (Percolation) * dt
INIT Cumulated_outflow = 0
INFLOWS:
Percolation = IF Open_or_closed=0 THEN
Inflow_in_lysimeters*EXP(Beta*LOGN(Soil_moisture/FC)) ELSE 0
Evaporation_cum(t) = Evaporation_cum(t - dt) + (Evapotranspiration) * dt
INIT Evaporation_cum = 0
INFLOWS:
Evapotranspiration = if Soil_moisture<FC then (Soil_moisture/FC)^alfa*Evapo else
evapo
immobile_cl(t) = immobile_cl(t - dt) + (immobilisation - mobilisation) * dt
INIT immobile_cl = 10
INFLOWS:
immobilisation = inflow*Cl_in_biomass
OUTFLOWS:
mobilisation = outflow*Cl_in_biomass
Obs_Cl_out_acc(t) = Obs_Cl_out_acc(t - dt) + (O_out_ut) * dt
INIT Obs_Cl_out_acc = 0
INFLOWS:
O_out_ut = obs_conc_cl_ut*O_p_konv
oxygen_content(t) = oxygen_content(t - dt) + (- oxy_outflow) * dt
INIT oxygen_content = Init_oxygen
36
OUTFLOWS:
oxy_outflow = Biomass_CC*met_qtnt
O_grund(t) = O_grund(t - dt) + (O_perk) * dt
INIT O_grund = 0
INFLOWS:
O_perk = Obs__perk
Soil_moisture(t) = Soil_moisture(t - dt) + (Inflow_in_lysimeters - Percolation Evapotranspiration) * dt
INIT Soil_moisture = 35.72
INFLOWS:
Inflow_in_lysimeters = Precipitation
OUTFLOWS:
Percolation = IF Open_or_closed=0 THEN
Inflow_in_lysimeters*EXP(Beta*LOGN(Soil_moisture/FC)) ELSE 0
Evapotranspiration = if Soil_moisture<FC then (Soil_moisture/FC)^alfa*Evapo else
evapo
alfa = 10
Beta = 2
Cl_in_biomass = .005
Conc_Cl_outflow = Cl_in_soil/Conversion_to_litre
Conversion_to_litre = Soil_moisture*8.011847/1000
Evapo = 0
FC = 93.92
Init_oxygen = 90
met_qtnt = .312
MOB_CONV = mobilisation/Conversion_to_litre
O_p_konv = Obs__perk*8.011847/1000
P_conversion_to_litre = Percolation*8.011847/1000
rate_of_Bdecrease = 0.5
rate_or_Bincrease = 1
rel_oxygen = oxygen_content/Init_oxygen
Cl_amout_in_precipitation_dep = GRAPH(TIME)
(0.00, 1.95), (1.00, 0.00), (2.00, 0.00), (3.00, 0.115), (4.00, 0.00), (5.00, 0.00), (6.00,
0.00), (7.00, 0.115), (8.00, 0.00), (9.00, 0.00), (10.0, 0.115), (11.0, 0.00), (12.0, 0.00),
(13.0, 0.00), (14.0, 0.115), (15.0, 0.00), (16.0, 0.00), (17.0, 0.115), (18.0, 0.00), (19.0,
0.00), (20.0, 0.00), (21.0, 0.115), (22.0, 0.00), (23.0, 0.00), (24.0, 0.115), (25.0, 0.00),
(26.0, 0.00), (27.0, 0.00), (28.0, 0.115), (29.0, 0.00), (30.0, 0.00), (31.0, 0.115), (32.0,
0.00), (33.0, 0.00), (34.0, 0.00), (35.0, 0.115), (36.0, 0.00), (37.0, 0.00), (38.0, 0.115),
(39.0, 0.00), (40.0, 0.00), (41.0, 0.00), (42.0, 0.115), (43.0, 0.00), (44.0, 0.115), (45.0,
0.00), (46.0, 0.00), (47.0, 0.00), (48.0, 0.00), (49.0, 0.115), (50.0, 0.00), (51.0, 0.00),
(52.0, 0.115), (53.0, 0.00), (54.0, 0.00), (55.0, 0.00), (56.0, 0.115), (57.0, 0.00), (58.0,
0.00), (59.0, 0.115), (60.0, 0.00), (61.0, 0.00), (62.0, 0.00), (63.0, 0.115), (64.0, 0.00),
(65.0, 0.00), (66.0, 0.115), (67.0, 0.00), (68.0, 0.00), (69.0, 0.00), (70.0, 0.115), (71.0,
0.00), (72.0, 0.00), (73.0, 0.115), (74.0, 0.00), (75.0, 0.00), (76.0, 0.00), (77.0, 0.115),
(78.0, 0.00), (79.0, 0.00), (80.0, 0.115), (81.0, 0.00), (82.0, 0.00), (83.0, 0.00), (84.0,
0.115), (85.0, 0.00), (86.0, 0.00), (87.0, 0.115), (88.0, 0.00), (89.0, 0.00), (90.0, 0.00),
(91.0, 0.115), (92.0, 0.00), (93.0, 0.00), (94.0, 0.115), (95.0, 0.00), (96.0, 0.00), (97.0,
37
0.00), (98.0, 0.115), (99.0, 0.00), (100, 0.00), (101, 0.115), (102, 0.00), (103, 0.00),
(104, 0.00), (105, 0.115), (106, 0.00), (107, 0.00), (108, 0.115), (109, 0.00), (110,
0.00), (111, 0.00), (112, 0.115), (113, 0.00), (114, 0.00), (115, 0.115), (116, 0.00),
(117, 0.00), (118, 0.00), (119, 0.115), (120, 0.00), (121, 0.00), (122, 0.115), (123,
0.00), (124, 0.00), (125, 0.00), (126, 0.115)
obs_conc_cl_ut = GRAPH (TIME)
(0.00, 0.00), (1.00, 0.00), (2.00, 0.00), (3.00, 1.32), (4.00, 0.00), (5.00, 0.00), (6.00,
0.00), (7.00, 1.60), (8.00, 0.00), (9.00, 0.00), (10.0, 0.00), (11.0, 0.00), (12.0, 0.00),
(13.0, 0.00), (14.0, 1.36), (15.0, 0.00), (16.0, 0.00), (17.0, 0.00), (18.0, 0.00), (19.0,
0.00), (20.0, 0.00), (21.0, 0.931), (22.0, 0.00), (23.0, 0.00), (24.0, 0.00), (25.0, 0.00),
(26.0, 0.00), (27.0, 0.00), (28.0, 0.585), (29.0, 0.00), (30.0, 0.00), (31.0, 0.00), (32.0,
0.00), (33.0, 0.00), (34.0, 0.00), (35.0, 0.558), (36.0, 0.00), (37.0, 0.00), (38.0, 0.00),
(39.0, 0.00), (40.0, 0.00), (41.0, 0.00), (42.0, 0.739), (43.0, 0.00), (44.0, 0.00), (45.0,
0.00), (46.0, 0.00), (47.0, 0.00), (48.0, 0.00), (49.0, 1.78), (50.0, 0.00), (51.0, 0.00),
(52.0, 0.00), (53.0, 0.00), (54.0, 0.00), (55.0, 0.00), (56.0, 2.05), (57.0, 0.00), (58.0,
0.00), (59.0, 0.00), (60.0, 0.00), (61.0, 0.00), (62.0, 0.00), (63.0, 2.19), (64.0, 0.00),
(65.0, 0.00), (66.0, 0.00), (67.0, 0.00), (68.0, 0.00), (69.0, 0.00), (70.0, 1.01), (71.0,
0.00), (72.0, 0.00), (73.0, 0.00), (74.0, 0.00), (75.0, 0.00), (76.0, 0.00), (77.0, 2.90),
(78.0, 0.00), (79.0, 0.00), (80.0, 0.00), (81.0, 0.00), (82.0, 0.00), (83.0, 0.00), (84.0,
3.15), (85.0, 0.00), (86.0, 0.00), (87.0, 0.00), (88.0, 0.00), (89.0, 0.00), (90.0, 0.00),
(91.0, 3.36), (92.0, 0.00), (93.0, 0.00), (94.0, 0.00), (95.0, 0.00), (96.0, 0.00), (97.0,
0.00), (98.0, 4.07), (99.0, 0.00), (100, 0.00), (101, 0.00), (102, 0.00), (103, 0.00),
(104, 0.00), (105, 2.59), (106, 0.00), (107, 0.00), (108, 0.00), (109, 0.00), (110, 0.00),
(111, 0.00), (112, 4.35), (113, 0.00), (114, 0.00), (115, 0.00), (116, 0.00), (117, 0.00),
(118, 0.00), (119, 5.24), (120, 0.00), (121, 0.00), (122, 0.00), (123, 0.00), (124, 0.00),
(125, 0.00), (126, 5.27)
Obs__perk = GRAPH (TIME)
(0.00, 0.00), (1.00, 0.00), (2.00, 0.00), (3.00, 24.1), (4.00, 0.00), (5.00, 0.00), (6.00,
0.00), (7.00, 4.50), (8.00, 0.00), (9.00, 0.00), (10.0, 0.00), (11.0, 0.00), (12.0, 0.00),
(13.0, 0.00), (14.0, 6.20), (15.0, 0.00), (16.0, 0.00), (17.0, 0.00), (18.0, 0.00), (19.0,
0.00), (20.0, 0.00), (21.0, 5.86), (22.0, 0.00), (23.0, 0.00), (24.0, 0.00), (25.0, 0.00),
(26.0, 0.00), (27.0, 0.00), (28.0, 7.82), (29.0, 0.00), (30.0, 0.00), (31.0, 0.00), (32.0,
0.00), (33.0, 0.00), (34.0, 0.00), (35.0, 9.23), (36.0, 0.00), (37.0, 0.00), (38.0, 0.00),
(39.0, 0.00), (40.0, 0.00), (41.0, 0.00), (42.0, 7.34), (43.0, 0.00), (44.0, 0.00), (45.0,
0.00), (46.0, 0.00), (47.0, 0.00), (48.0, 0.00), (49.0, 7.52), (50.0, 0.00), (51.0, 0.00),
(52.0, 0.00), (53.0, 0.00), (54.0, 0.00), (55.0, 0.00), (56.0, 7.03), (57.0, 0.00), (58.0,
0.00), (59.0, 0.00), (60.0, 0.00), (61.0, 0.00), (62.0, 0.00), (63.0, 7.19), (64.0, 0.00),
(65.0, 0.00), (66.0, 0.00), (67.0, 0.00), (68.0, 0.00), (69.0, 0.00), (70.0, 7.24), (71.0,
0.00), (72.0, 0.00), (73.0, 0.00), (74.0, 0.00), (75.0, 0.00), (76.0, 0.00), (77.0, 7.25),
(78.0, 0.00), (79.0, 0.00), (80.0, 0.00), (81.0, 0.00), (82.0, 0.00), (83.0, 0.00), (84.0,
7.25), (85.0, 0.00), (86.0, 0.00), (87.0, 0.00), (88.0, 0.00), (89.0, 0.00), (90.0, 0.00),
(91.0, 6.92), (92.0, 0.00), (93.0, 0.00), (94.0, 0.00), (95.0, 0.00), (96.0, 0.00), (97.0,
0.00), (98.0, 7.15), (99.0, 0.00), (100, 0.00), (101, 0.00), (102, 0.00), (103, 0.00),
(104, 0.00), (105, 7.02), (106, 0.00), (107, 0.00), (108, 0.00), (109, 0.00), (110, 0.00),
(111, 0.00), (112, 7.34), (113, 0.00), (114, 0.00), (115, 0.00), (116, 0.00), (117, 0.00),
(118, 0.00), (119, 7.44), (120, 0.00), (121, 0.00), (122, 0.00), (123, 0.00), (124, 0.00),
(125, 0.00), (126, 7.66)
38
Open_or_closed = GRAPH(TIME)
(0.00, 1.00), (1.00, 1.00), (2.00, 1.00), (3.00, 0.00), (4.00, 0.00), (5.00, 0.00), (6.00,
0.00), (7.00, 0.00), (8.00, 0.00), (9.00, 0.00), (10.0, 0.00), (11.0, 0.00), (12.0, 0.00),
(13.0, 0.00), (14.0, 0.00), (15.0, 0.00), (16.0, 0.00), (17.0, 0.00), (18.0, 0.00), (19.0,
0.00), (20.0, 0.00), (21.0, 0.00), (22.0, 0.00), (23.0, 0.00), (24.0, 0.00), (25.0, 0.00),
(26.0, 0.00), (27.0, 0.00), (28.0, 0.00), (29.0, 0.00), (30.0, 0.00), (31.0, 0.00), (32.0,
0.00), (33.0, 0.00), (34.0, 0.00), (35.0, 0.00), (36.0, 0.00), (37.0, 0.00), (38.0, 0.00),
(39.0, 0.00), (40.0, 0.00), (41.0, 0.00), (42.0, 0.00), (43.0, 0.00), (44.0, 0.00), (45.0,
0.00), (46.0, 0.00), (47.0, 0.00), (48.0, 0.00), (49.0, 0.00), (50.0, 0.00), (51.0, 0.00),
(52.0, 0.00), (53.0, 0.00), (54.0, 0.00), (55.0, 0.00), (56.0, 0.00), (57.0, 0.00), (58.0,
0.00), (59.0, 0.00), (60.0, 0.00), (61.0, 0.00), (62.0, 0.00), (63.0, 0.00), (64.0, 0.00),
(65.0, 0.00), (66.0, 0.00), (67.0, 0.00), (68.0, 0.00), (69.0, 0.00), (70.0, 0.00), (71.0,
0.00), (72.0, 0.00), (73.0, 0.00), (74.0, 0.00), (75.0, 0.00), (76.0, 0.00), (77.0, 0.00),
(78.0, 0.00), (79.0, 0.00), (80.0, 0.00), (81.0, 0.00), (82.0, 0.00), (83.0, 0.00), (84.0,
0.00), (85.0, 0.00), (86.0, 0.00), (87.0, 0.00), (88.0, 0.00), (89.0, 0.00), (90.0, 0.00),
(91.0, 0.00), (92.0, 0.00), (93.0, 0.00), (94.0, 0.00), (95.0, 0.00), (96.0, 0.00), (97.0,
0.00), (98.0, 0.00), (99.0, 0.00), (100, 0.00), (101, 0.00), (102, 0.00), (103, 0.00),
(104, 0.00), (105, 0.00), (106, 0.00), (107, 0.00), (108, 0.00), (109, 0.00), (110, 0.00),
(111, 0.00), (112, 0.00), (113, 0.00), (114, 0.00), (115, 0.00), (116, 0.00), (117, 0.00),
(118, 0.00), (119, 0.00), (120, 0.00), (121, 0.00), (122, 0.00), (123, 0.00), (124, 0.00),
(125, 0.00), (126, 0.00)
Precipitation = GRAPH (TIME)
(0.00, 81.1), (1.00, 0.00), (2.00, 0.00), (3.00, 4.80), (4.00, 0.00), (5.00, 0.00), (6.00,
0.00), (7.00, 4.80), (8.00, 0.00), (9.00, 0.00), (10.0, 4.80), (11.0, 0.00), (12.0, 0.00),
(13.0, 0.00), (14.0, 4.80), (15.0, 0.00), (16.0, 0.00), (17.0, 4.80), (18.0, 0.00), (19.0,
0.00), (20.0, 0.00), (21.0, 4.80), (22.0, 0.00), (23.0, 0.00), (24.0, 4.80), (25.0, 0.00),
(26.0, 0.00), (27.0, 0.00), (28.0, 4.80), (29.0, 0.00), (30.0, 0.00), (31.0, 4.80), (32.0,
0.00), (33.0, 0.00), (34.0, 0.00), (35.0, 4.80), (36.0, 0.00), (37.0, 0.00), (38.0, 4.80),
(39.0, 0.00), (40.0, 0.00), (41.0, 0.00), (42.0, 4.80), (43.0, 0.00), (44.0, 4.80), (45.0,
0.00), (46.0, 0.00), (47.0, 0.00), (48.0, 0.00), (49.0, 4.80), (50.0, 0.00), (51.0, 0.00),
(52.0, 4.80), (53.0, 0.00), (54.0, 0.00), (55.0, 0.00), (56.0, 4.80), (57.0, 0.00), (58.0,
0.00), (59.0, 4.80), (60.0, 0.00), (61.0, 0.00), (62.0, 0.00), (63.0, 4.80), (64.0, 0.00),
(65.0, 0.00), (66.0, 4.80), (67.0, 0.00), (68.0, 0.00), (69.0, 0.00), (70.0, 4.80), (71.0,
0.00), (72.0, 0.00), (73.0, 4.80), (74.0, 0.00), (75.0, 0.00), (76.0, 0.00), (77.0, 4.80),
(78.0, 0.00), (79.0, 0.00), (80.0, 4.80), (81.0, 0.00), (82.0, 0.00), (83.0, 0.00), (84.0,
4.80), (85.0, 0.00), (86.0, 0.00), (87.0, 4.80), (88.0, 0.00), (89.0, 0.00), (90.0, 0.00),
(91.0, 4.80), (92.0, 0.00), (93.0, 0.00), (94.0, 4.80), (95.0, 0.00), (96.0, 0.00), (97.0,
0.00), (98.0, 4.80), (99.0, 0.00), (100, 0.00), (101, 4.80), (102, 0.00), (103, 0.00),
(104, 0.00), (105, 4.80), (106, 0.00), (107, 0.00), (108, 4.80), (109, 0.00), (110, 0.00),
(111, 0.00), (112, 4.80), (113, 0.00), (114, 0.00), (115, 4.80), (116, 0.00), (117, 0.00),
(118, 0.00), (119, 4.80), (120, 0.00), (121, 0.00), (122, 4.80), (123, 0.00), (124, 0.00),
(125, 0.00), (126, 0.00)
res_dep = GRAPH (time)
(0.00, 1.00), (1.00, 1.00), (2.00, 1.00), (3.00, 1.00), (4.00, 1.00), (5.00, 1.00), (6.00,
1.00), (7.00, 1.00), (8.00, 1.00), (9.00, 1.00), (10.0, 1.00), (11.0, 1.00), (12.0, 1.00),
(13.0, 1.00), (14.0, 1.00), (15.0, 1.00), (16.0, 1.00), (17.0, 1.00), (18.0, 1.00), (19.0,
1.00), (20.0, 1.00), (21.0, 1.00), (22.0, 1.00), (23.0, 1.00), (24.0, 1.00), (25.0, 1.00),
(26.0, 1.00), (27.0, 1.00), (28.0, 1.00), (29.0, 1.00), (30.0, 1.00), (31.0, 1.00), (32.0,
1.00), (33.0, 1.00), (34.0, 0.00), (35.0, 0.00), (36.0, 0.00), (37.0, 0.00), (38.0, 0.00),
39
(39.0, 0.00), (40.0, 0.00), (41.0, 0.00), (42.0, 0.00), (43.0, 0.00), (44.0, 0.00), (45.0,
0.00), (46.0, 0.00), (47.0, 0.00), (48.0, 0.00), (49.0, 0.00), (50.0, 0.00), (51.0, 0.00),
(52.0, 0.00), (53.0, 0.00), (54.0, 0.00), (55.0, 0.00), (56.0, 0.00), (57.0, 0.00), (58.0,
0.00), (59.0, 0.00), (60.0, 0.00), (61.0, 0.00), (62.0, 0.00), (63.0, 0.00), (64.0, 0.00),
(65.0, 0.00), (66.0, 0.00), (67.0, 0.00), (68.0, 0.00), (69.0, 0.00), (70.0, 0.00), (71.0,
0.00), (72.0, 0.00), (73.0, 0.00), (74.0, 0.00), (75.0, 0.00), (76.0, 0.00), (77.0, 0.00),
(78.0, 0.00), (79.0, 0.00), (80.0, 0.00), (81.0, 0.00), (82.0, 0.00), (83.0, 0.00), (84.0,
0.00), (85.0, 0.00), (86.0, 0.00), (87.0, 0.00), (88.0, 0.00), (89.0, 0.00), (90.0, 0.00),
(91.0, 0.00), (92.0, 0.00), (93.0, 0.00), (94.0, 0.00), (95.0, 0.00), (96.0, 0.00), (97.0,
0.00), (98.0, 0.00), (99.0, 0.00), (100, 0.00), (101, 0.00), (102, 0.00), (103, 0.00),
(104, 0.00), (105, 0.00), (106, 0.00), (107, 0.00), (108, 0.00), (109, 0.00), (110, 0.00),
(111, 0.00), (112, 0.00), (113, 0.00), (114, 0.00), (115, 0.00), (116, 0.00), (117, 0.00),
(118, 0.00), (119, 0.00), (120, 0.00), (121, 0.00), (122, 0.00), (123, 0.00), (124, 0.00),
(125, 0.00), (126, 0.00)
40
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