The Tema Institute Department of Water and Environmental Studies ___________________________________________________________________

The Tema Institute Department of Water and Environmental Studies ___________________________________________________________________
The Tema Institute
Department of Water and Environmental Studies
___________________________________________________________________
Modeling Chloride Retention in Boreal Forest Soils – synergy of input
treatments and microbial biomass.
By
ONI STEPHEN KAYODE
LINKÖPINGS UNIVERSITET
___________________________________________________________________
Master of Science Thesis, Environmental Science Programme, 2007
Datum
Date
2007-06-01
Institutionen för Tema
Vatten i natur och samhälle
www.tema.liu.se
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Language
Svenska/Swedish
Engelska/English
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Report category
Licentiatavhandling
Examensarbete
C-uppsats
D-uppsats
Övrig rapport
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URL för elektronisk version:
http://www.ep.liu.se/index.sv.html
ISBN
__________________________________________
ISRN : LIU-TEMA/ES/D-07/04 - SE
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Serietitel och serienummer
Title of series, numbering
ISSN
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Modeling Chloride Retention in Boreal Forest Soils – synergy of
input treatments and microbial biomass
Supervisor
Per Sandén
Titel
Title
Modeling Chloride Retention in Boreal Forest Soils – synergy of input treatments and microbial biomass
Författare
Author
Oni Stephen Kayode
Sammanfattning
Abstract
The hypothetical assumption that chloride is conservative in the soil has been debated for the last decade. The results
of the recent years of study in chlorine biogeochemistry show that chloride is non-conservative but rather participates
in complex biogeochemical reactions in the soil. These interactions in nature inform the development of simplified
hydrochemical model of chloride dynamics in the soil that is driven on soil routine component of HBV hydrological
model. This novel attempt affords the opportunity to explore chlorine biogeochemistry further by evaluating the
biological processes such as microbial biomass that predominate chlorine cycles in the same order of magnitude as
earlier studied abiotic factors. Data from soil lysimeter experiment with different inputs treatments were used in the
calibration and validation of both the hydrological and biogeochemical model. The results show that (1) model
efficiency reduces with decreasing water residence and with increasing soil organic matter. (2) Longer water
residence time (low water input), high chloride and high nitrogen input loads relatively enhance maximum biomass
accumulation in a shorter time span. (3) Chloride retention time reduces with increasing chloride loads under short
water residence. (4) Microbial biomass growth rate is highest under high chloride input treatments. (5) Biomass death
rates shows reducing trend under short water residence (High water input). Further researches are therefore suggested
for possible model expansion and to make the results of this model plausible under field conditions.
Nyckelord
Keyword:
Biogeochemical model, Chlorine biogeochemistry, Chlorine cycles, Chloride immobilization, Microbial biomass and
water residence time.
ABSTRACT
The hypothetical assumption that chloride is conservative in the soil has been debated for
the last decade. The results of the recent years of study in chlorine biogeochemistry show
that chloride is non-conservative but rather participates in complex biogeochemical
reactions in the soil. These interactions in nature inform the development of simplified
hydrochemical model of chloride dynamics in the soil that is driven on soil routine
component of HBV hydrological model. This novel attempt affords the opportunity to
explore chlorine biogeochemistry further by evaluating the biological processes such as
microbial biomass that predominate chlorine cycles in the same order of magnitude as
earlier studied abiotic factors. Data from soil lysimeter experiment with different inputs
treatments were used in the calibration and validation of both the hydrological and
biogeochemical model. The results show that (1) model efficiency reduces with
decreasing water residence and with increasing soil organic matter. (2) Longer water
residence time (low water input), high chloride and high nitrogen input loads relatively
enhance maximum biomass accumulation in a shorter time span. (3) Chloride retention
time reduces with increasing chloride loads under short water residence. (4) Microbial
biomass growth rate is highest under high chloride input treatments. (5) Biomass death
rates shows reducing trend under short water residence (High water input). Further
researches are therefore suggested for possible model expansion and to make the results
of this model plausible under field conditions.
Keywords: Biogeochemical model, Chlorine biogeochemistry, Chlorine cycles, Chloride
immobilization, Microbial biomass and water residence time.
ACKNOWLEDGEMENT
I sincerely show my appreciation to Swedish Government for their kind gesture and for
providing this golden opportunity of free tuition master’s programmes. It has served as
terrific platforms for me to incubate my visions and aspirations to reality. I might not
have the best word of appreciation at the moment but I think I owe the nation my entire
life in gratitude. This is a legacy worth emulation!
I also give my great kudos to my supervisor, Per Sanden (Associate professor) for the
thought provoking discussions and helps both before and throughout the programme. I
hope we partner together beyond now. This statement of acknowledgement might not be
full without appreciating my Swedish mother, Ellinor Samuelson for her motherly care
and painting of smiles on my face at the rough start of my sojournment and
acclimatization process in Sweden.
The joint collaborative expertise of Olatunde Idris Ibikunle in this thesis and other
previous case studies is highly appreciated. To all my teachers and co-students in the
Department, it has been a real time together. Though the events might be over now but
the memory lingers on till ages. I give God the glory for seen me through the programme.
TABLE OF CONTENTS
ABSTRACT ...................................................................................................................................................2
ACKNOWLEDGEMENT ............................................................................................................................3
TABLE OF CONTENTS..............................................................................................................................4
INTRODUCTION.........................................................................................................................................5
1.1 BACKGROUND ..............................................................................................................................5
1.2 AIMS AND OBJECTIVES .............................................................................................................6
1.3 WHY CHLORINE? .........................................................................................................................6
1.4 STATE OF THE ART ......................................................................................................................8
1.5 GAPS IN KNOWLEDGE ................................................................................................................9
METHOD ....................................................................................................................................................11
2.1 SITE AND EXPERIMENTAL DESCRIPTION .........................................................................11
2.2 DATA DESCRIPTION ..................................................................................................................11
2.3 MODEL CONCEPTUALIZATION ............................................................................................12
2.3.1 Hydrological model ................................................................................................................13
2.3.2 Biogeochemical model ...........................................................................................................13
2.6 MODEL CALIBRATION AND VALIDATION ........................................................................15
2.6.1 Hydrology.................................................................................................................................15
2.6.2 Biogeochemistry ......................................................................................................................16
RESULTS ....................................................................................................................................................18
3.1 HYDROLOGICAL MODEL ........................................................................................................18
3.2 BIOGEOCHEMICAL MODEL ....................................................................................................18
3.3 MICROBIAL BIOMASS C RESPONSES.................................................................................23
DISCUSSION ..............................................................................................................................................27
4.1 MODEL ESTIMATES OF CHLORIDE RETENTION-RELEASE..........................................27
4.2 MODEL EFFICIENCY R2 AS AN INDICATOR .......................................................................28
4.3 BIOMASS QUANTIFICATIONS IN SOIL LYSIMETER TREATMENTS ...........................29
4.4 MODEL LIMITATIONS ..............................................................................................................31
CONCLUSION............................................................................................................................................32
REFERENCE ..............................................................................................................................................33
APPENDIX A: .............................................................................................................................................36
4
INTRODUCTION
1.1 Background
The sustenance of our contemporary ecosystem has conferred high demands of
interdisciplinary collaboration on the scientific world to tackle the complicated problems
facing our dying environment. In the study of elemental cycling and fluxes in our
biospheric world, many factors controlling these cyclic processes are yet to be unraveled.
This is of particular importance in the modeling of the past and presently generated data
to understand and make projections in our biogeochemical world. These detail dynamics
of elemental fluxes, cyclic and transformations in the environment are important to
understand how nutrients, pollutants etc flow and the reactions of ecosystem to such
changes. Among the elements involved in the biogeochemical studies, only few such as
nitrogen, carbon, and oxygen etc are better studied and their dynamics in the environment
better known.
Others such as chlorine are less studied, especially in relation to biological processes such
as microbial biomass influences on input-output balance of chloride in the soil.
Literatures have attributed the relative dependence of microbial biomass more on
resource availability, soil properties or chemistry than soil types (Bauhus and Khanna,
1999). In this thesis, hypothesize is thus set that inorganic chloride might be one of those
influential soil chemical properties that might limit microbial biomass growth in the soil.
This represents a new line of research as this speculation is further buttressed with an
earlier report by Ågren et al. (1996) that microbial biomass usually choose the inorganic
form of elements for their metabolisms. Others factors reported in the literatures to have
influence on the microbial biomass in the soil include climatic variables (Friedel et al.,
2006) and changing substrate quality and quantity (Qingchao et al., 2004). Also of
importance are heavy metals, soil pH, seasonal changes and vegetation or plant cover
(Wardle, 1992) as well as soil moisture and temperature (Bauhus and Khanna, 1999). All
these have certain degree of influence on the microbial biomass variability in the forest
soils.
Thus, there are many interdependent processes involved in the study of microbial ecology
and biogeochemical cycling of elements in our biospheric world. This imposes a
limitation on the development of fully distributed biogeochemical model of chloride
transport in the soil. Past researches have based the hydrochemical model development
on the hypothesizes that chlorine is conservative in the soil and is therefore employed as
a tracer of other ions, water origin and for budget and deposition estimates with a
presumption that chloride input equals the output (Rodstedth et al., 2003) and does not
adsorbed to organic matter in the soil (Lovett et al., 2005). However, recent decades of
researches conducted in the biogeochemical study of chlorine showed that chloride
undergoes complex biogeochemical cycles in nature and does not exhibit conservative
nature in the soil (Bastviken et al., 2006; Öberg and Sanden, 2005; Öberg et al., 2005;
Bastviken et al., 2007) against previous hypothetical assumption.
Therefore, the full understanding of interactive abiotic and biological influences on
chloride biogeochemical cycles in the environment still represents a considerable gap in
knowledge that requires further attentions. Then can the development of explanatory and
predictive model with greater predictive prowess be facilitated. This will detail the true
behavioral pattern of chloride transformation and cyclic processes in the soil in order to
5
break the paradigm lock between the two competing hypothetical assumptions of
conservative (e.g. Schlesinger, 1997) and non-conservative behavior of chloride. This is
particularly important, as chloride is reported to be actively present in many of our
industrial products (Mario and Rowland, 1974) with its associated effect on ozone layers
and climate change, amidst other ecological consequences (Richard and Bernard, 2002).
1.2 Aims and Objectives
The overall goal of this thesis is to develop a simplified chloride biogeochemical model
that will generate a more comprehensive understanding of chlorine biogeochemistry. This
will, in accordance with previous researches, confer relative importance on the
immobilization-mobilization processes that appears to drive chloride imbalances in the
soil. This imbalance in chloride budget estimates resulted from differing rates of chloride
retention-release in the soil due to differences in input treatments of the soil in lysimeters
under study. Specific objective of this thesis will be:
•
•
•
To evaluate and test if chloride might also be a limiting factor for microbial
biomass growth in the soil.
To estimate the synergistic effect of soil input treatments (chloride, nitrogen and
precipitation input loads) and microbial biomass on the chloride retention-release
of the soil as well as chloride retention time.
Relative comparison of microbial biomass growth and death rates in each treated
lysimeters.
These objectives would be achieved by developing a coupled hydrologicalbiogeochemical model of chloride dynamics in the soil, detailing microbial biomass and
oxygen sub-models. This will help to further identify missing gaps in chlorine
biogeochemistry and processes that govern chloride cycling in the soil for possible future
expansion of the model.
1.3 Why chlorine?
Chlorine was discovered in Köping, Sweden by a Swedish chemist Carl Wilhelm Scheele
in 1774 and got its present name since 1810 by Sir Humphry Davy (Encyclopaedia
Britanica, 1986). It is categorized to the family of highly reactive group seven elements in
the periodic table called halogens with only Fluorine being more reactive in the halogen
family (Encyclopaedia Britanica, 1986). It is ranked 18 in position out of the 92 most
abundant naturally occurring elements on earth (Lovett et al., 2005; Öberg, 1998). The
high electron affinity imposes high reactivity on chlorine and this account for its rare
occurrence in free states. Chlorine can be established in reactive forms with other
elements and compounds as chloride ion (Cl ) and can be found in the atmosphere, water
bodies, sediments, vegetation, microorganisms and in the soil (Svensson, 2006). Chlorine
is also reported to be a component of organic matter and it is ranked to be sixth abundant
in organic matter following phosphorus, nitrogen, carbon, oxygen and hydrogen (Öberg,
2002).
The electronegative property of chlorine thus makes it easily form solutions in soil pore
water and in the process percolates through the soil, carrying the soluble chloride along
the soil profile. This process leads to the leaching of salts and other polar substances and
of organic chlorine. The leaching of the latter is reported to be in accordance with the
leaching of organic matter in the same magnitude as deposition in the soil (Öberg, 1998).
6
This makes chlorine to be ubiquitous in nature and facilitated its occurrence in the
different speciation as inorganic chloride (Clin) and organic chlorine (Clorg), Volatile
Organo-Chloride compounds (VOCls) such as chloroform (CCl4) and chlorinated organic
compounds such as trichloroacetic acids and can be found bounded to both fulvic and
humic acids as well (Svensson, 2006; Öberg, 2002). Therefore, chlorine is regarded in
this thesis as phenomenal entity that entails both the Clin and Clorg.
Several environmental forcing(s) such as physical and chemical factors drives the relative
occurrence of chlorine in the environment. Such factors include wind, water, weathering,
precipitation, ion exchange etc (Öberg 2003) and this widespread occurrence shows the
relative importance of chlorine in the biogeochemical cycles (Öberg, 2002). Inorganic
chloride influx to the environment has been attributed to many sources. For example
Lovett et al. (2005) attributed the main influx of Clin to the soil of Hubbard Brook
Experimental Forest Station in 1960s to be from pollutants such as coal burning until
1970s when the contribution from this pristine source declined and remains predominant
marine sources. The contribution of Clin from the marine sources result when the ocean
currents or waves breaks, resulting in the aerosol formation which can subsequently drop
as dry deposition on the soil, or wash down by the precipitation as wet deposition
(Svensson, 2006). In the case of organic chlorine, pristine fluxes to the environment have
been reported to be from natural formation, vegetation, plant litters and thoroughfall
(Öberg, 1998; Öberg et al., 2005; Myeni, 2002).
Chloride is as well regarded as essential nutrient in the majority of living organisms and
is therefore categorized as micronutrient. It serves in regulating the osmotic balance in
cells, electro-potential balance in the central nervous system and as anion exchanger in
organisms as well as oxidizing and chlorinating agent in living organisms (Öberg, 1998).
For example, chlorination of tyrosine containing protein is reported to strengthen the
cuticles and adhesive properties of protein sheets in invertebrates (Öberg, 2002). It
works in the enzymatic synthesis of reactive hypochloride or hypochlorous acid (HOCl)
used in the intracellular defense (Öberg, 2002) in organisms. The formation of this
reactive HOCl is reported to also occur in the soil in the presence of hydrogen peroxides.
This resulting HOCl imposes reactivity on chloride to oxidize the organic substrate and in
the process enhances the biodegradation of the organic matter (OM) in question. Traces
of Clin are also found in the rock and it is reported to be one of the essential minor
elements of plant nutrients (Lovett et al., 2005). The participation of chlorine in essential
metabolic processes such as photosynthesis, could as well not be under-emphasized
(Öberg, 2005).
The presence and the widespread use of chlorine-containing industrial products such as
PVC, PCB, CFC, plastics, organic chemicals, algaecides, flame retardant compounds etc
represent vital anthropogenic inputs by man’s activities. Also, industrial processes such
as grease removal, bleaching in Pulp and Paper manufacturing processes, agricultural
post harvest disinfection, drinking as well as swimming water disinfection etc represent
major anthropogenic flux to the environment (Encyclopaedia Britanica, 1986; Grimvall,
1995). The steady natural sources of Clin in combination with these anthropogenic inputs
represent the total pool of Clin flux to the environments (Mario and Rowland, 1974;
Thomsen, 2006; Svensson, 2006). For example, pulp mill alone is reported to have the
potential of increasing the Clorg up to the dangerous concentration level of 1mgL-1
downstream its location (Svensson, 2006). Chloroform, which is a by-product of
industrial activities such as Pulp bleaching and water chlorination etc, can severely
7
impair drinking and swimming water quality well above the safety standard as well as
destruction of ozone layers (Svensson, 2006; Juuti, et al., 1996). This constitutes great
environmental hazards to both terrestrial and aquatic ecosystems and has drawn great
attentions in the scientific world from the past decades. These motivate the reason why
more researches should be done on chloride biogeochemical cycles in the environment.
1.4 State of the art
In the study of the biogeochemical cycling of chlorine in the ecosystem, past researches
have hypothesized that chlorine is conservative (Schlesinger, 1997) i.e. it does not
undergo any biological and chemical reactions in soil. This hypothetical assumption led
to the logical inference in the study of chlorine cycle and its subsequent adoption as a
tracer of water and other ions in the soil. Recent researches in chlorine biogeochemistry
have falsified this earlier hypothetical paradigm on which earlier scientists operate by
showing that chloride does not exhibit conservative behavior in the soil. The research of
Öberg and Sanden (2005) on chloride retention in the soil showed that chloride is
organically bound to and retained in the soil. In another research, Öberg et al. (2005)
showed that considerable quantities of organic chloride is leached from topsoil and
precipitated at the lower soil profile. This makes topsoil layer to potentially act as
chloride sink and deeper soil zones a source (Svensson, 2006).
The pool of chlorine in the soil pore water was also reported to be in the form of
inorganic chloride and the dominance of organic chlorine as soil storage (Öberg and
Sanden, 2005; Svensson, 2006). The latter is due to chlorination of soil organic matter
(SOM) by microorganisms and the resulting mobile SOM is subsequently mineralized to
release the SOM-bound chloride. The output chloride leachate of the soil from
decomposing organic matter thus dominates the transport process. The chloride
immobilization and flux out of the soil therefore appears to be in correlation with the
degradation potentials of the SOM by microbes (Öberg, et al., 2005). Therefore, the
mobility of chloride through the soil is hindered by its assimilation into the organic
matter, which is followed by precipitation in deeper soil level. The already incorporated
organic chlorine, which is organically bound in the soil, can remain in the organic matter
for months or even century unless it is mineralized. Though knowledge of this
dechlorination process is still low however, reports have shown (e.g Öberg, et al., 2005;
Öberg and Sanden, 2005; Bastviken et al., 2007) that some forms of microorganisms are
able to mineralize SOM-held chloride in the soil, a function that depends on degradability
and quality of SOM.
In another reviewed publication, Öberg, (2002) affirmed the abundance of organic
chlorine in relative magnitude of inorganic chloride in the soil against the four widely
accepted old paradoxical-paradigm that chloride only occurs in ionic form, is xenobiotic,
is toxic and persistent in the environment (Grimvall, 1995). This was in line with the
outcomes of another research conducted by Rodstedth et al. (2003) on soil lysimeters.
They observed that soils in some of the lysimeters served as a sink while some served as
source of chloride. Their observation of chloride imbalances in the lysimeters experiment
further strengthens the earlier observation of Johansson et al. (2000) on the same forest
soil that organic chlorine storage in the soil is about four times larger than soil inorganic
chloride.
8
Lovett et al. (2005) have equally shown in their forest harvesting experiments that clear
cutting tend to increase the leaching of chloride in the catchment they studied. Their later
hypothesis was that transport of chlorine was in balance between transformation and
transportation processes. These start from surface chlorination of organic matter and
transportation of the chlorinated organic matter down the soil horizons. At the deeper soil
zones, chlorinated SOM is mineralized (dechlorination) and subsequent transported
groundwater to the surface waters. This is also in contrary to the earlier assumptions of
the conservativeness and direct passage of chloride in the soil. Therefore, it can be
deduced from their experimental results that catchment’s disturbances such as harvesting
of forest can increase the mineralization of organic matter, thus unleashing the chloride
that had been previously incorporated into the soil organic matter.
Svensson (2006) also showed with clear evidences that chlorine species are involved in
various transformation and complex biogeochemical processes. She inferred the chloride
retention and release occurring simultaneously in the soil both at the laboratory and onsite field study and that soil water origin, water residence time and seasonal variations
influence the chloride balance as well as water discharges. The chloride import-export
fluxes of the soil thus appear to be substantially different in short-term perspective
(Bastviken et al., 2006). Further research is therefore necessary to fully understand the
influential factors; both intrinsic and extrinsic that mediates the chloride transformation
and transportation processes in the soil. This also adds to the clue of non-conservative
involvement of chloride in complex biogeochemical cycles than posed by the earlier
postulates.
The very recent research of Bastviken et al. (2006) however postulated that the observed
chloride retention-release in their experiment is more of temporal functions and water
residence time and that it might be due to the selective microbial degradation of SOM
and microbial oxygen consumption in the process. These alter the redox potential in the
soil and consequently influence the chloride concentration in the leachate. Thomsen,
(2006) also supports the involvement of oxygen in the immobilisation of chloride in the
soil. Recent research of Bastviken et al. (2007) further confirms the dependence of
chloride retention-release on time, temperature, soil depth, oxygen regime and microbial
actions. These latter factors have formed the framework for the development of this
model to take a step further in the understanding of chlorine biogeochemistry.
1.5 Gaps in knowledge
Biogeochemical modelling of chloride transformation in the soil detailing immobilization
processes still represent a big gap in knowledge as chloride cycles requires more
processes than we presently know. This has formed the primal objective of this thesis by
collating the relatively few and scattered pieces of information on chloride
immobilization processes in the soil to develop a simplified running chloride model. As
earlier reviewed, Bauhus and Khanna, (1999) made a comparative quantification of
microbial biomass carbon in similar forest soils. They observed wide differences in the
relative quantities of microbial biomass in the forest soils and they therefore suggested
that other factors such as soil chemical properties than carbon might limit the microbial
biomass growth in the forest soils. This represents a missing knowledge in microbial
ecology and thus forms a hypothetical framework in this study that chloride might be one
of those few suggested soil chemicals that imposes limitation on microbial biomass
growth in the soil. This organic carbon in the soil and inorganic chloride had been
9
previously shown by Öberg and Sanden (2005) to be highly correlated. Further
researches are needed in this area to give a comprehensive understanding of this biomass
and chloride speculations.
Further study of Bastviken et al. (2007) on chloride retention in forest soil further
affirmed the prompt involvement and uptake of inorganic chloride by micro-organisms as
the primary cause of chloride retention-release in the studied soil. This shows that
microbial activity in soil is dependent on availability of readily degradable soil organic
matter. They came up with hypothesis that inorganic chloride in soil pore water reduces
by 25% for every 10% increase in microbial biomass population. Microbial biomass is
used in this study as a phenomenon that entails fungi, bacteria, algae and other microbes.
Also, speculative involvement of oxygen on chloride immobilization in the soil
(Thomsen, 2006; Bastviken et al., 2006, 2007) also informed the development of
coupling oxygen model. Detailed direct field measurements and relationships between
microbial biomass C, chloride retention and oxygen consumption still stand a gap in
knowledge that needs to be fully filled.
10
Method
2.1 Site and Experimental description
The data from experimental results of Bastviken et al. (2006) on chloride retentionrelease in their soil lysimeter experiment were used in the calibration and validation of
the model. The soils used in the lysimeter experiment were collected towards end of June
2001 from coniferous forest of Stubbetorp catchment. This catchment is located at (580
44’N, 160 21’E) southeast of Sweden with total area of about 0.87km2(Bastviken, et al.,
2006). The catchment consists of predominantly Norwegian spruce (Picea abies) and
Scots pine (Pinus sylvestris) with spodosol-type of soil that is rich in organic matter. The
mean annual precipitation and temperature of the catchment is also reported to be around
600mm and 6oC respectively (Maxe, 1995).
Soils were sampled at a location (about 2.5m2) that was close to the discharge area within
the catchment (Bastviken et al., 2006). The lysimeters were of equal cross sectional area
of 80cm2 and were irrigated with artificial rain twice a week. The contents of the artificial
rain, which includes SO42-, Ca2+, Na+ etc were prepared in accordance with the observed
precipitation in Stubbetorp area. The lysimeters were incubated in dark chambers and the
experiment was carried out for a period of 127 days. The data were collected every three
or four days.
There were eight different lysimeter treatments in triplicates, resulting in a total of 24
different lysimeters. The lysimeters were treated with differing amounts of Chloride,
Nitrogen and Water inputs in accordance with the observed load within Stubbetorp area
(corresponding to low input treatments) and contribution from western coast
(corresponding to high input treatments) of Southern Sweden respectively (Bastviken et
al., 2006). Nitrogen was specifically chosen in addition to chloride and water load as
earlier research of Rodstedth et al. (2003) had hypothesised that nitrogen might influence
the retention of chloride in the soil. Table 2.1 show the factorial design of lysimeters’
input treatments.
High and low water treated lysimeters were irrigated with a total of 4032ml and 1344ml
of artificial rain respectively over the entire 127 days of the experiment. Alternated high
and low Clin input lysimeters corresponds to total amount of 12.1mg Cl- lysimeter-1 and
4.0 mg Cl- lysimeter-1 respectively for the duration of the experiment. The lysimeters with
high Nitrogen load correspond to 5.7 mg N lysimeter-1 and those with low Nitrogen
inputs equal 1.6 mg N lysimeter-1 (See table 2.1 below).
2.2 Data description
The building of the model was principally based on the data generated from a soil
lysimeter experiment conducted by Bastviken et al. (2006). The data were collected from
chlorine research group of Department of Water and Environmental Studies, Linkoping
University. Since the experimental data were not taken every day and the running of the
model requires all data points, a time series of the data on daily time step were generated
in SPSS for the whole period of 127 days that the experiment took place. Days without
data measurement were ascribed in STELLA modeling software as missing values and
replaced with zeros. This was done so that the inserted zeros would not affect running of
the model. The running of the models was also based on 123 days data points as first four
days in the data were removed to have fairly equal starting points for the observed data
11
and the simulations due to large initial amount of water that was added to lysimeters to
reach field capacity.
Table 2.1: Summary table showing factorial design of lysimeter treatments
2
High
3
Low
4
High
5
Low
6
High
7
Low
8
High
Low
Low
Water
High
1
Chloride
Low High Low
Nitrogen
High
Lysimeters
2.3 Model Conceptualization
The aim and objective of this thesis was achieved by the development of a dynamic
model of chloride transport in the soil. Modelling software, STELLA version 6, was used
as a platform for the development of this model. The competing hypothesis and available
primary data from Bastviken et al. (2006), observations from Bastviken et al. (2007) and
other researches on microbial biomass (e.g Bauhus and Khanna, 1999 and Friedel et al.,
2006) informed the conceptualization of this model. The data showed that chloride
actually undergoes some biogeochemical reactions in the soil and retention-release is
time dependent. Thus, the conceptualization of the model entails the chloride transport
and immobilization processes in the soil against the earlier presumed direct passage of
chloride in the soil (See appendix A for a representative STELLA codes used to calibrate
lysimeter 1A). The model was calibrated on the eight specific and differently treated
lysimeter to evaluate the influence of each treatment on chloride outputs. However, the
calibration of the parameters used in the model was validated with either of the two
replicates of each lysimeter treatments. Thus, the establishment of relationships and the
development of this model were based on referenced literatures.
The entire conceptualized model of chloride dynamics used in this study was divided into
two independent but coupled sub-models viz:
• Hydrological Model
• Biogeochemical model
The Biogeochemical model is further divided into:
o Chloride Transport: This entails the transportation of chloride through the
soil, starting from input to the output of the soil lysimeters.
o Chloride transformation: For the scope of this thesis and based on relevant
literatures, two important sub-model components are developed:
§ Biomass model
§ Oxygen model.
12
2.3.1 Hydrological model
HBV is a semi distributed conceptual runoff model that can be easily modified to
different hydrological conditions both at laboratory and catchment scales. Thus, the
hydrological model used in this study employs the use of only the soil routine component
of the simplified HBV runoff model (Bergström et al., 1985). Snow routine and
groundwater routine component of the runoff model were eliminated as the experiment
was conducted at laboratory scale. The model also assumed evaporation as an important
hydrological process that is relevant to the experimental condition and data under
scrutiny and is run on daily time step. The building of hydrological sub-model was
adopted because of polarity of Clin and their solubility in water. This makes water to
serve as a veritable means of transport for both Clin and chlorinated soil organic matters
etc (Svensson, 2006; Öberg and Sanden, 2005 and Öberg, 1998). Simulation results of
HBV model could be described to well explain the hydrology of the lysimeter treatments
due to the observed strong model efficiency in both high and low water lysimeters. This
makes the model to be a good estimator and a reliable driver of the entire biogeochemical
model for the estimation of chloride amount in the soil and water balance studies. The
model efficiency, denoted as R2, was calculated in accordance with the estimates of Nash
and Sutcliffe (1970) using the formula below:
n
∑ (Qcom (i ) − Qobs (i ))
R 2 = 1− i =1 n
∑ (Qobs (i ) − Qobs )
2
( −∞ < R 2 ≤ 1)
2
i =1
Where:
R2 = Model efficiency
Qcom(i) = Model outputs
Qobs(i) = Observed outputs
Qobs = Observed mean outputs
2.3.2 Biogeochemical model
The biogeochemical model component is driven on the hydrological model for estimation
of chloride amounts in the soil using soil moisture balance and chloride loads from the
model. This informs the development of chloride transport and transformation model as
shown in Figure 2.1. The chloride transport system details the transportation from the
input load to the leachate out of the soil lysimeters. The transformation solely occurs in
the soil core and is an integral part of the chloride transport systems that accounts for
chloride retention and release in the soil. Though, several researches have hypothesized
various chloride immobilization processes in the soil, however, many immobilization
processes were not considered because of the experimental set up of the data used. For
example, chloride immobilization due to vegetation uptake is eliminated in the model.
This was due to the laboratory scale of the experiment that as well took place in the dark
chamber (Bastviken et al., 2006). Thus, the influence of microbial biomass and oxygen
shall only be modeled in addition to the soil input treatments. This choice is also due to
the scarcity or unavailability of data to model other processes. Therefore, direct field
measurement of chloride uptake by microbial biomass at the catchment level is
suggested.
13
2.3.2.1 Biomass sub-model
Chloride retention-release results from both immobilization and mobilization processes in
soil. Bastviken et al. (2007) hypothesized that microbial biomass growth in the order of
10% can immobilized about 25% of Clin concentrations in the soil porewater. This
suggests that microbial biomass growth enhances Clin retention (immobilization) in the
soil while microbial biomass death favours the release (mobilization) of the immobile
chloride back to the soil porewater. Other researches (e.g. Raubuch and Beese, 1999)
have demonstrated the relative dependence of microbial biomass C growth on metabolic
quotient and oxygen content of the soil. This informs the conceptualization of the
microbial biomass and oxygen sub-model as part of the transformation processes that are
responsible for chloride retention-release in soils. Since there is no comparative estimate
of microbial biomass assimilation of chloride in the soil, this forms the basic framework
for the development of microbial biomass sub-model as part of the extension of the main
chloride transport model.
W
A
T
E
R
E
V
A
P
O
Cl
&
N
SOIL CORE
LYSIMETER
TREATMENTS
H
Y
D
R
O
L
O
G
Y
B
Clin I
H20
T
R
A
N
S
P
O
M
A
S
S
Clorg
Immobi
lization
O2
O
R
T
Mobilizat
ion.
Clin
P
E
R
C
Cl2 amount in leachate
1.6cm
Figure 2.1: Schematic representation of the conceptual model
14
15cm
2.6 Model calibration and validation
In modeling, it is of importance to fix the experimental data set to model simulations so
as to conform the model to the specific characteristic behaviour of the system in question.
In order to achieve this, there are some sets of parameters that need to be adjusted for the
model to suit the system under study by increasing the degree of conformity of the model
simulation and observed data. This process, which is called calibration, was thus
performed by visual examination of the simulations and the observed differences. The
parameter sets that were used in the calibration of this model might not produce a perfect
simulation of the observed data due to rather involving complex biogeochemical
processes that imposes variations that the model could not explain at this scale. Thus
parameterization of the calibration was based on literature values in order to produce a
model with an acceptable degree of agreement with nature. Model efficiency (R2) was
estimated to evaluate the strength of the calibration as well as the estimate of volume
error for the hydrological model. This model accuracy was tested by validating it with the
replicate treatments of respective lysimeter to evaluate how much of the observed
variations the model could explain and to avoid forcing of the probable error assumptions
in the course of the model calibration. Below is the list of the model parameters that were
used for the calibration and validation processes.
2.6.1 Hydrology
1. Model variables
Potential Evapotranspiration: This is a supposed input variable for the model but was
changed to calibrated parameter because of lack of data and non-direct measurement of
this process in the laboratory. The ideal value range at catchment scale is 0 - 0.3 mm
day-1 oC-1 (Lindström et al., 1997) but a value of 0.38mm was used in this model.
Actual Evapotranspiration: This is an output variable and it considers evaporation as
the most important hydrological process applicable to the empirical data under
consideration. This variable is a function of soil moisture content, FC and potential
evapotranspiration.
Precipitation: The precipitation data used in the development and running of this model
is in the form of artificial rain added to the soil lysimeters in the laboratory. The rain
was added to the lysimeters every three or four days. Data for the first four days were
not used to stabilize initial water condition in lysimeters.
Percolation: This was simulated against the observed percolation data in each lysimeter
treatments. It is responsive to the soil moisture content, precipitation and the BETA
parameter. This was used to estimate the chloride amount in the outflow out of the
lysimeters.
2. Model Initial Values
Soil moisture: Soil moisture on its own demands complex models to fully describe its
dynamics (Lindström, et al., 1997) but this has been simplified in this study in order to
adapt it to the model scale. Initial soil moisture contents were estimated by the
difference in the wet and dry weight of the soil.
3. Model parameters
BETA is one of the soil routine calibration parameters that describe soil particle
arrangement in order to control the relationship between the soil moisture and discharge
or percolation. High BETA decreases the percolation at low water content in the soil
15
and vice versa. The BETA calibration values range from 2-4 (Seibert, 2002) in
catchment applications.
FC: This is a model parameter and can be defined as maximum soil moisture content or
storage capability. It does not mean that this value be necessarily equal to the measured
value of Field Capacity and thus can be calibrated. However, this value was equated to
initial soil moisture of lysimeters in this study as excess water was drained prior to start
of experiment. Typical values normally used in calibration of this parameter range from
100mm and can be up to 300mm but the value of 83.5mm used to calibrate this model
was adopted to fit the scale of this study.
2.6.2 Biogeochemistry
1. Model variables
Input Loads: This encompasses the chloride and nitrogen loads that were added as
treatments of each lysimeter. The relative amount of chloride and nitrogen input in
precipitation was in form of artificial rain or irrigation in the laboratory. The data on
chloride input was principally used to run the biogeochemical model and the input
amount varies from 0.115mg and 0.350mg for low and high loads respectively. Data on
nitrogen inputs were not used in the model, as the conceptualization of the model could
not accommodate them due previous observation of Bastviken et al. (2006) that
nitrogen might not have large impact.
Chloride Output: a pristine model output variable that evaluates the amount of chloride
in the leachate of the lysimeters. This was simulated for each lysimeter treatments using
the observed chloride amount in the output. The model utilizes the generated
percolation values to estimate chloride amounts in the leachate.
Transformation Process: This entails chloride mobilization and immobilization
processes in and out of soil pore water pool as shown in figure 2.1. Immobilization
process results in retention of chloride as long as microbial biomass increases. Thus,
functioning of this process is dependent on microbial biomass growth and amount of
chloride in the growing biomass. Mobilization on the other hand is connected to death
of microbes to drives the retained chloride back to soil pore water.
2. Initial values
Chloride in soil: The estimate of initial chloride content of the soil was evaluated by
the difference between the added amount of chloride from precipitation with the initial
chloride in the soil before the treatment and chloride in the leachate. This was done to
equilibrate the starting point for each lysimeter model but it appears that this
assumption was overestimated as the simulations started well above the observed
chloride outputs. The analysis of Bastviken et al. (2006) on the same data equally
recognized this discrepancy and reported that some chloride might have been
immobilized in the soil prior to the attainment of field capacity. This therefore poses a
limitation for the estimate of initial chloride amount in the soil and therefore forms a
weak point of the model.
Microbial Biomass C: The initial microbial biomass population used in the model was
based on the referenced literature, as there is no direct measurement of magnitude of
microbial biomass in the forest soil in question. Therefore literatures were searched for
the comparative estimate of microbial biomass C quantification in the similar forest soil
in another region. The initial value of 100mg per 80cm2 cross sectional area of
lysimeter (equivalent to 12.5 g m-2) of biomass used in the model falls within the
16
reported range of 12 - 422g m-2 for forest soils (Bauhus and Khanna, 1999; Raubuch
and Beese, 1995, 2005; Friedel et al., 2006).
Initial Oxygen content: Reports have shown the relative dependence of microbial
biomass growth on the availability of oxygen (Thomsen, 2006; Bastviken et al., 2007).
This motivates the reason for the inclusion of oxygen component in the model but it
was difficult to find relevant literature values for the setting and calibration of this
parameter.
3. Model parameters
Microbial Biomass growth rate: This is a key model parameter in the microbial
biomass component that determines the immobilization of Clin out of the soil pore
water and is regulated by oxygen content of soil alone as generated from oxygen model.
Microbial biomass grows as long as there is oxygen and chloride-bound organic matter
substrate in the soil.
Microbial Biomass death rate: This parameter is responsible for the biomass death
when the resource is depleted. It thus accounts for the mobilization of chloride back to
the soil pore water.
Microbial Metabolic Quotient: This parameter denotes respiration to biomass ratio
and thus works in close collaboration with the microbial biomass growth rate. A fixed
value of 0.045 mg O2 d-1 mg-1 was used in the calibration of this model parameter. This
falls to the equivalent range reported by Raubuch and Beese (1999).
17
RESULTS
3.1 Hydrological model
The results of the simulations were summarized for both the biogeochemical model
(including calibration and validation) as well as the representative hydrological models.
The hydrological model developed can be assumed to well describe the hydrology of the
lysimeters irrespective of the different water input loads. Sensitivity analysis was also
performed on the hydrological model by changing the field capacity and the moisture
contents for all the lysimeter treatments. The results of sensitivity analysis show no
variation in hydrological models of all lysimeters. Estimated model efficiency R2 for the
hydrological model in all lysimeter was about 1 with negligible volume errors. Thus, the
hydrological model of lysimeter 1A was chosen here as a representative model for all the
lysimeters. R2 values were calculated in accordance with the earlier described estimate of
Nash and Sutcliffe, (1970). Figure 3.1 shows the modeled percolation (cumulative) and
cumulative observed percolation respectively.
Hydrological Model
140
Percolation (mm)
120
100
80
Model
Observed
60
40
20
0
-20 0
20
40
60
80
100
120
140
Days
Figure 3.1: Simulation of the cumulated percolation in the lysimeter 1A over the period of the experiment.
3.2 Biogeochemical model
The biogeochemical model was also calibrated for each of the lysimeter specific
treatments and was validated by either of the replicates. The results showed the same
pattern of chloride behaviour in all the treatments chosen. Chloride outputs from the
lysimeter treatments dropped for about 40 days of the experiment before rising again.
Figure 3.2 - Figure 3.10 show the time series simulations for the respective treatments
used in this study. The R2 values were also estimated using Nash and Sutcliffe, (1970).
The previously introduced zeros for the missing days in the original data while running
the model in STELLA modelling software with their corresponding simulated values
were not included in the calculation of R2 as it tends to keep the model efficiency below
zero.
18
Figure 3.2 to 3.5 show the model results of low water lysimeter with an alternating low
and high chloride and nitrogen inputs. Figure 3.2 shows the chloride model of lysimeter 1
under low precipitation, low chloride input and low nitrogen. The two models have close
R2 values due to high conformity of the data. These two representative lysimeter
treatments showed two outliers that occurred nearly the same days in both lysimeters and
were included in evaluating the model efficiency. Initial chloride output of lysimeter 1a
started around 2 mg/l while lysimeter 1c started from 1 mg/l. Both tend to have nearly the
same low chloride output plateau before rising again after 27.2 days of experiment.
Lysimeter 1a
Lysimeter 1c
Chloride (mg/L)
5
Chloride (mg/L)
R2=0.75
6
4
Observed
3
model
2
1
R2=0.83
7
6
5
4
Observed
3
model
2
1
0
0
0
50
100
0
150
50
100
150
Days
Days
Figure 3.2: Simulation and observed chloride output for lysimeter 1a (calibration) and 1c
(validation) under low Precipitation, low Chloride and low Nitrogen.
From Figure 3.3, the result shows that the parameterization of the calibration model
(lysimeter 2a treatment) could only explain a minute part of the observed variations in the
validation (lysimeter 2b treatment) model. The chloride output of lysimeter 2a treatment
reached zero before picking up after 34 days.
Lysimeter 2b
Lysimeter 2a
R2= 0.75
6
5
Observed
4
Model
3
R2= 0.02
7
Chloride (mg/L)
Chloride (mg /L)
7
2
1
6
5
4
Observed
3
Model
2
1
0
0
0
50
100
0
150
50
100
150
Days
Days
Figure 3.3: Simulation and observed chloride output for lysimeter 2a (calibration) and 2b (validation)
under Low Precipitation, Low Chloride and High Nitrogen.
It appears that the model simulation of chloride output in lysimeter 3 shown in Figure 3.4
performs better. The observed data showed close conformity in pattern with higher
chloride outputs in initial leachate, therefore the model could better explain the observed
variations in data. The chloride output reached the lowest plateau of 3.61mg/L of chloride
in the first 24.4 days as compared with the lower values observed in the two previously
examined lysimeters in figure 3.2 and 3.3. The results also show approximately equal R2
values in both the calibration and validation lysimeters.
19
Lysimeter 3b
Lysmeter 3a
R2= 0.92
12
12
10
8
Observed
6
Model
R2= 0.85
14
Chloride (mg/L)
Chloride (mg/L)
14
4
2
0
10
8
Observed
6
Model
4
2
0
0
50
100
150
0
50
Days
100
150
Days
Figure 3.4: Simulation and observed chloride output for lysimeter 3a (calibration) and 3b (validation)
under Low Precipitation, High Chloride and Low Nitrogen.
Model results of lysimeter 4 treatments shown in Figure 3.5 were similar to observation
from lysimeter 3 under the same input treatment influence of low precipitation and high
chloride. R2 values are about the same; no outlier value was observed and no difference
in the starting points in both the simulated and the observed chloride outputs. The slopes
were less steep and minimum chloride output of about 5.34mg/l was reached in 34.9 days
in both lysimeters before rising.
Lysimeter 4a
Lysimeter 4c
R2= 0.92
12
10
8
Observed
6
model
R2= 0.89
14
Chloride (mg/L)
Chloride (mg/L)
14
4
2
0
12
10
8
Observed
6
model
4
2
0
0
50
100
150
0
Days
50
100
150
Days
Figure 3.5: Simulation and observed chloride output for lysimeter 4a (calibration) and 4c (validation)
under Low Precipitation, High Chloride and High Nitrogen.
Figure 3.6 through figure 3.9 present the model results for the second major water
treatment division i.e. high water treatments with alternating low and high chloride and
nitrogen input loads. Also see table 2.1 for the design and the treatments of the
lysimeters. General overview of the results in comparison with the low water lysimeters
show reducing R2 values that occasionally approach negative, more diffuse pattern of the
observed chloride data set and reduced chloride amounts in the leachate.
Model result of lysimeter 5 treatments shown in Figure 3.6 begins this series of highwater input lysimeters. The lysimeter treatment used in the calibration and validation of
this model were equally treated with low chloride and low nitrogen inputs. The result
shows reduced amount of chloride in the output of the lysimeters to around a value of
20
0.27mg/L in 31.2 days of the experiment. Variation in observed data is more pronounced
as R2 value is further reduced in the validation lysimeter.
Lysimeter 5c
Lysimeter 5a
R2= 0.53
2
1.5
Observed
Model
1
R2= 0.131
2
Chloride (mg/L)
Chloride (mg/L)
2.5
0.5
1.5
Observed
1
Model
0.5
0
0
0
50
100
0
150
50
100
150
Days
Days
Figure 3.6: Simulation and observed chloride output for lysimeter 5a (calibration) and 5c (validation)
under High Precipitation, Low Chloride and Low Nitrogen.
Figure 3.7 depicts the model of chloride output in lysimeter 6 treatments under the same
condition of high water, low chloride but with high nitrogen treatment. The model
efficiency R2 further reduced and even approach negative value in the validation
(Lysimeter 6a treatment). This might indicate that there are more processes that led to the
wider variation in the observed data that the model could explain under this treatment
condition. The difference in the starting point for both simulated and observed chloride
output becomes more pronounced. The observed chloride amount in the leachate of the
lysimeter 6b treatment dropped to nearly 0mg/L in 31.5 days before picking up again.
Lysimeter 6b
Lysimeter 6a
Chloride (mg/L)
Chloride (mg/L)
R2= 0.35
2
1.5
Observed
1
Model
0.5
0
0
50
100
150
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
R2= -0.69
Observed
Model
0
Days
50
100
150
Days
Figure 3.7: Simulation and observed chloride output for lysimeter 6b (calibration) and 6a (validation)
under High Precipitation, Low Chloride and High Nitrogen.
Model of chloride output in treatment of lysimeter 7 in Figure 3.8 shows more
conformity in pattern than treatment of lysimeter 6 in figure 3.7. Three similar outliers
were also observed. The calibration parameters of lysimeter 7b could not explain the
observed variation of chloride outputs in lysimeter 7c (validation) treatment due to
observed negative R2 value in the model result. However, the results show that the
observed chloride amount in the output of the two representative treatments drop to about
0.2 mg/L in day 24.4 of the experiment.
21
Lysimeter 7b
Lysimeter 7c
R2= 0.56
4
3
Observed
2
Model
Chloride (mg/L)
Chloride (mg/L)
5
1
0
0
50
100
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
150
R2 = -0.13
Observed
Model
0
50
Days
100
150
Days
Figure 3.8: Simulation and observed chloride output in lysimeter 7b (calibration) and 7c (validation) under
High Precipitation, High Chloride and Low Nitrogen.
In lysimeter 8c and 8a shown in Figure 3.9, the pattern of the observed chloride output in
both treated lysimeters was slightly different with chloride immobilization approaching
nearly zero. The model efficiency R2 is averagely better, showing improvement of the
model over the last three lysimeters under high water loads. The chloride outputs dropped
until around 24.1 days before it starts to rise again.
Lysimeter 8c
Lysimeter 8a
2
R = 0.59
4
3
Observed
2
Model
R2= 0.45
6
Chloride (mg/L)
Chloride (mg/L)
5
1
0
5
4
Observed
3
Model
2
1
0
0
50
100
150
0
Days
50
100
150
Days
Figure 3.9: Simulation and observed chloride output for lysimeter 8c (calibration) and 7a (validation)
under High Precipitation, High Chloride and High Nitrogen.
In an attempt to make a comparative estimate of simulated amount of chloride in the
output of each treatment, the results of the simulations from Figure 3.2 to Figure 3.9 were
collated to a comparative chart in Figure 3.10. The result shows that treatment of
lysimeter 3 and lysimeter 4 (denoted as L3 and L4) under low precipitation and high
chloride treatment have the highest chloride output. L5 and L6 under high precipitation
and low chloride have the least chloride output. This shows the observed difference
between the lysimeter treatments under low-high precipitation and low-high chloride
loads. The observed chloride outputs in L1-L2 (low precipitation and low chloride) and
L7-L8 (high precipitation and high chloride) are within the same range despite the wide
difference in the precipitation and chloride input loads. However, chloride retention and
release in L5 and L6 were very close and this same pattern was observed in L7 and L8 as
well.
22
Simulation of chloride transport in lysimeters
14
L4
12
L3
Chloride (mg)
10
8
L2
6
4
L1
L8, L7
2
L6,L5
0
-2
0
20
40
60
80
100
120
140
Days
Figure 3.10: Relative trend of simulated chloride transport in the representative lysimeters for
each treatment. L1 to L8 denote lysimeter 1 to lysimeter 8 respectively.
3.3 Microbial Biomass C responses
In addition to the simulation of chloride behaviour for each treatment of the lysimeters,
microbial biomass C responses to each of the treatments were also evaluated in the
model. Figure 3.11 shows the comparative simulated responses of microbial biomass C to
different input treatments in all the studied lysimeters and its ultimate impacts on chloride
retention-release. This was done in order to evaluate how each of the lysimeters treatment
or input loads might influence the microbial biomass C growth in the soil. However, this
result will only paint a picture of likely impact of chloride input loads on microbial
biomass accumulation in the field.
The result shows wide variations in the biomass accumulations and biomass peak attained
over time. The result indicates that input treatments of high chloride, high precipitation
and high nitrogen in lysimeter 8 (L8) seem to mostly favour the biomass accumulation.
Lysimeter 5 (denoted as L5) shows least build up with time under the input treatments of
high precipitation, low chloride and low nitrogen. As the model of these two lysimeter
treatments are fair; they can comparatively represent the minimum and maximum range
of model results.
23
1000
900
800
700
600
500
400
300
200
100
0
L8
L3
L1
L2
10
4
11
2
12
0
96
88
80
72
64
56
L4
48
40
32
24
L7
L6
L5
8
16
0
Biomass accumulation (mg)
Relative Biomass accumulation for all lysimeters
Days
Figure 3.11: Comparative presentation of modelled microbial biomass C
accumulations in all the lysimeters. L1-L8 represents Lysimeter 1 to Lysimeter
8 respectively.
The observed result from Figure 3.11 is also indicative of microbial biomass reaching
different magnitude of peaks at different time spans. This informs the relative comparison
of these growth peaks and length of time it took to arrive at those peaks in each of the
treatments as shown in Figure 3.12. This helps to evaluate the lysimeter input treatments
that might be more suitable for microbial biomass build up with time in relation to the
retention-release of chloride in the soil. The result shows that the lysimeters under high
chloride inputs mostly favor biomass accumulation. Biomass peaks increases with higher
nitrogen inputs except in L3-L4 where decrease is observed. The observed maximum
biomass growth peak of lysimeter 8 (L8) treatments was reached in 22 days while the
lysimeter 5 (L5) shows least growth peak at a lengthier time span of 37 days. Therefore,
time span for biomass to reach its peaks increases from low nitrogen to high nitrogen
inputs except in L7-L8 comparing series.
24
Comparison of days to attain maximum Biomass
increase in lysimeters
1000
50
800
40
600
30
Days
Biomass peak (mg)
Comparison of Simulated Biomass Peaks for all
lysimeters
400
20
10
200
0
0
1
2
3
4
5
6
7
1
8
2
3
4
5
6
7
8
Lysimeter Number
Lysimeter Number
Figure 3.12: Comparison of biomass peaks (simulated) and the days it took to reach the peaks in
respective lysimeters
A similar comparison of the modeled growth and death rate parametric values in each of
the lysimeter treatments are shown in table 3.1. The result also shows that biomass
increase rate in low water lysimeter treatments were relatively about the same range but
increases with high chloride inputs in high water load lysimeters. Biomass rate of
decrease were high in low water lysimeter treatments except in treatment of lysimeter 3.
High water load lysimeters on the contrary showed decreasing trend from L5 to L8. This
shows the best growth and death rate values used in the parameterization of this model. It
is however difficult to evaluate the effect of nitrogen inputs on this rates. It can be
deduced from the table of parameter values that lysimeter 2 (low water, low chloride and
high nitrogen) and lysimeter 4 (low water, high chloride and high nitrogen) were
calibrated with slightly higher oxygen value in the model. However, there is no relevant
literature for comparison of this oxygen parameterization as used in the model. This
therefore forms weak point of the model.
Table 3.1: Lysimeter treatments and summary of parameter values.
Precipitation
Chloride
Load
LOW
LOW
HIGH
Nitrogen
Input
Biomass
increase Rate
Biomass
decrease Rate
O2
0.100
0.020
500
0.095
0.019
850
0.121
0.008
600
0.085
0.015
850
0.070
0.015
500
0.065
0.010
500
0.125
0.008
500
0.155
0.004
500
LOW
*(1)
HIGH
(2)
HIGH LOW
(3)
HIGH
(4)
LOW LOW
(5)
HIGH
(6)
HIGH LOW
(7)
HIGH
(8)
(mg)
Table 3.1: Summary of the model parameter values for oxygen content of soil, biomass increase and decrease rates and
factorial design of the experiment.
* The number in parenthesis represents corresponding lysimeter numbers.
25
Model efficiency R2, chloride retention and retention times were summarised in the Table
3.2. The result shows decreasing model efficiency from low-water input lysimeters to
high water-water input lysimeters. Treatments of lysimeter 3 and 4 under the same
influence of low water and high chloride have the highest chloride retention and
subsequent release. The result also shows the retention time increasing with high nitrogen
inputs in low-water input lysimeters and decreasing from low chloride input to high
chloride inputs in high-water lysimeters. However, the factors responsible for this shift in
retention time are not full known. Further researches are therefore recommended to
understand these processes.
Table 3.2: Summary table for model efficiency and cumulative chloride outputs
Precipitation Chloride
Load
LOW
LOW
Nitrogen
Input
Lysimeter
Number
+
LOW 1
HIGH 2
HIGH LOW 3
HIGH 4
HIGH
LOW
LOW 5
HIGH 6
HIGH LOW 7
HIGH 8
A
C
A
B
A
B
A
C
A
C
B
A
B
C
C
A
2
*R2
Chloride
retention (mg)
Retention time
(day)
0.750
0.826
0.752
0.018
0.919
0.850
0.920
0.886
0.526
0.131
0.347
-0.685
0.561
-0.130
0.593
0.449
0.27
27.2
0.26
34.0
3.61
24.4
5.34
34.9
0.27
31.2
0.20
31.5
0.20
24.4
0.37
24.1
* R denotes model efficiency and were calculated in accordance with Nash and Sutcliffe, (1970)
+
Letters denote replicate of lysimeter treatments that were used in calibration and validation
sequentially
26
Discussion
4.1 Model estimates of chloride retention-release
The result of the biogeochemical model further broadens the understanding of chloride
cycling in the soil and strengthens the non-conservative nature of chloride. The cycling
and the transformations of chloride in the soil involve more complex biogeochemical
reactions than we presently know. This was shown in the model results that there are
many underlining and interactive factors governing both chloride immobilization and
mobilization in the soil porewater. This non-conservative nature of chloride had been
earlier observed from the researches conducted at both laboratory (Oberg and Sanden,
2005; Rodstedth et al., 2003) as well as catchment experimental scales (Nyberg et al.,
1999; Viers et al., 2001; Lovett et al., 2005 and Likens, 2005).
The highlighted aim and objectives of this thesis was thus achieved by the successful
development of a simplified hydrochemical model of chloride transport in the soil
lysimeters (see the model simulations Figure 3.2 to Figure 3.10). The results of this study
show that chloride outputs in each lysimeter treatments respond differently to different
lysimeter inputs and that chloride concentration in the leachate dropped in the first few
days of the experiments. The model for all lysimeters successfully reproduced this same
scenario of chloride behaviour. This has been a premier attempt to model chloride
transport in the soil as no evidence of previous attempts was reported in literatures. This
forms a veritable platform for future expansion and prompts for recognition of other
research areas that might require attention in the study of chlorine biogeochemistry
Results show that chloride retention and release can occur simultaneously in the soil. The
descending parts of the model results indicate chloride withdrawal (retention) from soil
porewater while the rising part shows chloride release (mobilization) back to the soil
porewater (Bastviken et al., 2006). The model results also informed the chloride
retentions occurring at the timescale of about 24-35 days in all the lysimeters. Several
factors have been reported to regulate these temporal variations in the soil. These include
but not limited to soil organic matter quality, soil microorganisms, soil types and
chemical properties of the soil etc (Thomsen, 2006; Bastviken et al., 2006, 2007).
However, the time dependence of chloride retention-release was further confirmed in
another recent research conducted by Bastviken et al. (2007) on chloride retention in soil.
This shows that time as well as soil input-output balance has great influence on chloride
biogeochemical cycles.
In accordance with Figure 3.10, the comparative model results of the relative magnitude
of chloride outputs under each lysimeter input treatments show that input treatments of
lysimeter 3 and 4 (low water and high chloride) show higher chloride retention and
subsequent high release of chloride. On the contrary, it appears that lysimeter 5 and 6
(high water and low chloride) have lowest chloride retention and release back to the soil
pore water. This shows that longer water residence time (low water treatment) has greater
influence on the retention-release of chloride than the short water residence (high water
treatments). L1, L2, L7 and L8 occupy middle range as observed in figure 3.10. It
therefore appears that longer water residence time (low water loads) plus high chloride
inputs have greater effect on the chloride retention-release in the soil than short water
27
residence and low chloride inputs. This implies that longer water residence provides
enough platforms for biological and chemical reactions to take place between microbial
biomass and soil organic matters. It appears that high-chloride lysimeter under the
influence of high nitrogen have slightly higher chloride retention-release than low
nitrogen lysimeter treatments. However, the reverse was noticed in low-chloride
lysimeters as higher nitrogen inputs reduce chloride retention. Although, differences in
these comparing series are not so wide, this could account for the reason why Bastviken
et al. (2006) could not as well observed any impact of nitrogen loads on the chloride
outputs of the lysimeters. Therefore, their argument is supported that low dose of
nitrogen concentration was used as input treatments of the lysimeters to have an
appreciable effect on chloride outputs. This indicates the importance of soil deposition
and seasonal influences on chloride retention and release when extrapolated to catchment
scales.
No concrete evaluation of O2 parameterization could be made from the model results
shown in table 3.1, as there is no relevant literature to compare the parameter values with.
However, slightly higher oxygen values calibration worked well for lysimeter 2 and 4
treatments under the influence of high nitrogen. Past researches have equally evaluated
the impact of oxygen involvement on chloride retention in the soil. For example, Viers et
al. (2001) had noted higher oxygen content in the soil of Mengong River watershed
during dry season, which can be assumed to correspond to low water input in this
lysimeter experiment, and reduction in oxygen during wet season (assumed to correspond
to high water input). Similar laboratory experiment of Thomsen, (2006) on
immobilisation of inorganic chloride in the soil showed higher chloride retention under
oxic than anoxic conditions. However, the reliability of this model assumptions and
possible future expansion depend on further researches, thereby making oxygen
involvement still speculative.
The general overview of the chloride retention time in all the lysimeter treatments shown
in Table 3.2 is an indication of temporal influences on chloride retention-release in the
soil. In low water input lysimeters, it appears that low and high chloride loads under the
same influence of high nitrogen show an increasing trend of chloride retention time. On
the other hand, observation of the high-water lysimeters show that chloride retention time
reduces from low chloride to high chloride input loads. No large difference between the
chloride retention and chloride retention time in lysimeter treatment 5 and 6. This is
equally observed in Lysimeter 7 and 8, showing the chloride behaviour under short water
residence.
2
4.2 Model efficiency R as an indicator
The estimated model efficiency (R2) measures the strength of the model for each of the
lysimeters (see Figure 3.2- 3.5 and Table 3.2). The model efficiencies were relatively
high in low-water lysimeters. On the other hand, large difference was observed between
the model calibration and validation in lysimeters under high water loads. The results
therefore show reducing model efficiency from low-water lysimeters to high-water
lysimeters. This shows that the model could better explain the variations observed in the
low-water input lysimeters than high-water input lysimeters. This implies that microbial
driven model cannot solely explain the observed variations in chloride retention-release
28
at this water regime. It thus appears that more processes than biological factors are
involved in the soil that the simplicity of this model cannot account for at low water
residence time.
This further support the inference made on the same data by Bastviken et al. (2006) that
water residence time has greater influence on chloride retention and release in the soil in
question. This is an indication that other underlining factors such as soil properties might
influence chloride retention and release in the soil. This informs the reason for probing
into the original data to crosscheck for the likely factors that might influence the observed
unexplained variations by the model in some of the lysimeters. The observation showed
that soil organic matter content of the soils in the lysimeters differs greatly. It appears that
the model efficiency in high-water lysimeters reduce with increasing soil organic matter
(SOM) content of the soils. This implies that other processes (e.g. SOM) than oxygen
limits microbial activity that account for net retention-release at this water residence time.
This result is contrary to the observation of Bastviken et al. (2006) as they could not
establish any relationship between chloride retention-release and the SOM. However, no
clear trend in model efficiency could be observed in low-water input lysimeters in
relation to SOM. Therefore, possible future incorporation of soil organic matter into the
model is suggested.
4.3 Biomass quantifications in soil lysimeter treatments
The relevance of microbial activities on chloride retention-release in the soil has been
shown in literatures to depend on substrate availability, oxygen availability and soil
organic matter (Thomsen, 2006; Bastviken et al., 2006, 2007). Therefore, microbial
biomass variability and its impact on chloride output in each lysimeter-specific treatment,
represents a new line of analysis in the study of biogeochemistry of chlorine in the soil.
This is of particular importance as there is no previous direct field measurement of
microbial biomass uptake of chloride in the soil. More research is needed for more
biogeochemical processes to be incorporated into the model to enhance its applicability to
field conditions. The parameterization of this model was based on relevant literatures
describing biomass growth and oxygen consumption in the soil.
The observed maximum biomass values in the model range from 340 - 923 mg (42.5 –
115 gm-2). These values fall within the equivalent range reported in literatures for forest
soils (Bauhus and Khanna, 1999; Raubuch and Beese, 1995, 2005). The exponential
pattern of microbial biomass growth observed in the model also conforms to literatures.
For example, Killam, (1994) observed similar pattern of growth in their study of
microbial population dynamics under closed, batch system and the microbial exponential
curve was thus compartmentalized into four phases. These phases include lag phase,
exponential phase (phase of unlimited growth), stationary phase (phase of limited
growth) and death phase. Killam, (1994) further suggested the condition for the optimal
growth of microbes in close vessels such as the soil lysimeters used in this study to
include substrate quality, temperature, pH and more importantly, water potential.
Bauhus and Khanna, (1999) also reported large variations in the quantity of microbial
biomass in their review of literatures on microbial biomass C growth in forest soils. They
therefore hypothesized that several factors such as climate variability and soil chemical
29
properties might influence microbial biomass quantification in the forest soil. Model
results thus suggest the relevance of chloride as one of those speculated soil nutrients that
might likely influence microbial biomass accumulation in the soil. This therefore give a
clarion call for further research in this area in order to better detail the biological
processes that might predominate chlorine biogeochemistry in the same order of
magnitude as earlier studied abiotic factors.
In accordance with Figure 3.11, the results of microbial biomass responses to different
water stress (low and high inputs) and chloride loads showed that lysimeter (L8) under
high precipitation, high chloride and high nitrogen loads mostly favor biomass
accumulation over time. This was also observed in lysimeter three (L3) under the same
condition of high chloride load but low water and low nitrogen inputs. Though the detail
processes governing this is not yet understood, but the model results point that high
chloride content of soil tend to have greater influence on soil microbial biomass increase.
This hypothetical speculation needs to be further tested.
Lysimeter five (L5), treated with low chloride and high precipitation least favored
biomass build up. Lysimeter one (L1) under the same low chloride input but also with
low water treatment followed L5 in the minimum range in support of biomass
accumulation. L2, L4, L6 and L7 fall within the middle range, all of which were under
high nitrogen loads except L7. It thus appears that nitrogen might play a mild role that
seems inhibitory under these conditions. The model thus predicts the limiting potential of
chloride on microbial growth; as high chloride inputs tend to favor the biomass build up
with time than low chloride input lysimeters.
This serves as a powerful indicator of significance of soil chloride deposition and
seasonal influences on microbial population and chloride fluxes in environment. This also
shows conditions that can make soil microbial biomass to act as source or sink of
chloride in soils. As there is no previous research on this, relative comparison of this
result cannot be made at the moment until future but this will inform the basic hypothesis
to be tested in future researches in the biogeochemical study of chlorine in the soil. The
comparative analysis of parameter values used in the calibration of the model for biomass
increase and decrease rate in Table 3.1 also shows that biomass growth rate is at the
highest under the input treatment in lysimeter 8. This is followed by treatments in
lysimeter 3 and 7. The microbial biomass death rate reduces from high-water lysimeter 5
to 8.
It can also be deduced from the model result in Figure 3.12 that it took microbial biomass
in each lysimeter treatments different number of days to attain their maximum peaks.
This informs the relative comparison of the attained biomass peaks and the corresponding
number of days it took biomass to reach these peaks under each lysimeter treatments. The
results showed that biomass build up reached the highest peak in lysimeter 8 to a value of
about 923mg and it took about 22 days to attain this peak. Lysimeter 5 has the least
biomass peak (340mg) and it took about 37 days before the peak could be attainment.
However, comparison of the biomass build-ups in Figure 3.11 and chloride outputs in
Figure 3.10 show that treatment of lysimeter that favor high biomass accumulation does
not necessarily have the highest chloride outputs. For examples, high chloride retention30
release was observed in L3 and L4 but their biomass accumulations were not the highest.
Observation of L7 and L8 show that the two treatments have moderately low and close
chloride outputs but their biomass accumulation with time was wide with L8 having the
maximum biomass. The factors responsible for these variations are unknown and thus
require further test.
General overview of all the lysimeter treatments shows that biomass peak attainments in
low water lysimeters were relatively higher compared with high water lysimeters. High
water lysimeters show increasing trend of biomass peaks from low chloride inputs to high
chloride inputs. Large impact of Nitrogen on biomass accumulation could not be
detected. It can be deduced that it took treatments with high biomass growth lesser time
period to arrive at the peaks while low biomass peaks took more days to arrive at their
minimal peaks. The biomass values in the model results do not exceed the range of
reported equivalence of 12 - 422 gm-2 in the literatures (Bauhus and Khanna, 1999;
Raubuch and Beese, 1995, 2005). These modeled biomass values were converted to
referenced catchment scale unit for easy comparison. The model results therefore point
that low water (high water residence time) and high chloride inputs mostly enhance
maximum biomass accumulation in the soil within the shorter time span than high water
(low water residence time) and low chloride loads. This shows the relative importance of
chloride and water loads on the microbial biomass growth in the soil. This poses a
research front for further investigations on how microbial biomass and chlorine cycles
can interact in the field conditions for more comprehensive understanding of microbial
ecology and chlorine biogeochemistry.
4.4 Model limitations
The novelty nature of this study to develop a working chloride model has made it to
strive in the midst of challenges, starting from collating scattered pieces of information to
major assumptions taken in the model. Though the model affords the opportunity to
further d chlorine biogeochemistry and poses research fronts in areas that needed further
attention. However, scarcity and unavailability of data on other biogeochemical processes
impose limitations on the model. These include:
•
•
•
•
•
Inability of the model to account for some of the observed variations due to
unavailability of data to model other influential biogeochemical processes.
Model overestimation of initial chloride content of the soil
Underestimation of microbial activities that is solely limited by oxygen
availability in the model.
Scarce literatures to make relative comparison of oxygen model parameterization.
Scarce literature for comparison of model results
Further study is thus suggested at catchments scales for model extension that will
incorporate more biogeochemical processes that limit the model ability to account for the
observed variations in chloride outputs of soil.
31
Conclusion
Modelling chloride retention and transport in soil showed that chloride cycling in nature
is dependent on many interactive factors acting simultaneously at a time. The observed
shift in net chloride retention and release in the model results show that chloride cycles is
a function of temporal factor. Also, soil input-output balance has been shown to have
pronounced effect on net shift in retention-release of chloride. However, the flux in the
ecosystems tends to strike a reasonable balance between many under-explored processes
acting over a long-term span if not anthropogenically disturbed
Microbial biomass growth might also be potentially limited by chloride availability in the
ecosystem. This is evident in the model results as microbial biomass responses to
different chloride input loads treatments are wide enough to call for further
investigations. This shows that biological mediated processes are equally important like
other abiotic factors on chloride net retention and release in the soil. These could as well
be suggested to explain part of the fundamental differences between the two hypothetical
assumptions of conservative and non-conservative of chloride in the soil.
Model results also emphasised the influence of water residence time on net chloride
retention and chloride retention time in soil. These show that chloride retention is under
the influence of many complex biogeochemical interactions acting simultaneously in the
soils viz: soil depositional inputs, time, water residence and microbial mediated
processes. Inabilities of the model to explain part of the observed chloride outputs further
strengthen the active involvement of other processes on chloride retention-release that
were not presently included in the model. Further researches are therefore suggested for
possible future expansion and development of a fully distributed model of chloride
dynamics in the soil with great predictive prowess.
32
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35
APPENDIX A:
Representative STELLA codes as used in calibrating lysimeter 1A.
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
OUTFLOWS:
oxy_outflow = Biomass_CC*met_qtnt
O_grund(t) = O_grund(t - dt) + (O_perk) * dt
INIT O_grund = 0
36
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, 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),
(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),
(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),
(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),
(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),
(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),
(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),
(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),
(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),
(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),
(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),
37
(7.00,
(15.0,
(23.0,
(31.0,
(39.0,
(47.0,
(55.0,
(63.0,
(71.0,
(79.0,
(87.0,
1.60),
0.00),
0.00),
0.00),
0.00),
0.00),
0.00),
2.19),
0.00),
0.00),
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)
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,
38
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), (39.0, 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)
39
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