BELOW I SHARE THE DRAFT OF MY WORK, MYSELF DID THE PARTS FROM INTRODUCTION TO METHODOLOGIES, IM STRUGGLING WITH RESULTS AND DISCUSSION PART. THATS WHY I COME HERE TO ASK FOR HELP. I HAVE ALL THE DATA YOU NEED, ALL THE EXCELL FILES THAT CAN HELP YOU TO GENERATE GRAPHS AND HELP ME WITH RESULTS PART AND DISCUSSION ALSO CONCLUSION ART.
Department of Humanity and Environmental Studies,
National Dong Hwa University
Seminar Report 3
Evaluating Carbon, Water, and Energy Budgets Using Eddy Covariance
Technique in a Solar Panel Farm: Implications of Land Use Change from Forest to
Solar Farm
Nsia
Asanterabi Ulomi（611155004）
Advisor:
Prof. Shih-Chieh Chang
June, 2024
Contents
1.2 Carbon,
Water, and Energy Budget 6
2. Research
Objectives and Hypothesis. 11
3.1.1 FLSF and DNDF (Hualien-Taiwan) 12
3.2 Data
acquisition and processing. 14
3.2.1 Tower and Sensor Information. 14
4.1 Meteorological
results. 19
4.2 Temporal Variation in Carbon, Water, and Energy Fluxes. 19
4.3 Comparison of Flux Measurements between Sites. 22
4.4 Correlation Analysis and Environmental Drivers. 23
5.1 Interpretation of Findings. 24
5.2 Comparison with Previous Studies. 24
5.3 Limitations and Future Research Directions. 24
Abstract
The
conversion of forested land into solar panel farms represents a significant
land use change with potential implications for carbon sequestration, water
dynamics, and energy exchange. This study utilizes eddy covariance measurements
to evaluate the impacts of land use change from forest to solar farms on
carbon, water, and energy budgets. Data collected from both the forest and
solar panel farm sites are analyzed to understand the variations in flux
dynamics and assess the environmental implications of the transition. The
findings provide insights into the effects of land use change on ecosystem
processes and inform sustainable land management practices in the context of
global warming mitigation. ***(NEED SOME WORK DONE)
Introduction
1.1
Background
Global warming is a critical issue that has become a
major concern worldwide. A range of environmental impacts, including sea level
rise, extreme weather events, and ecosystem changes, are expected to result
from human activities leading to global warming of 1.0°C above pre-industrial
levels. Global warming would most likely exceed 1.5°C between 2030 and 2052 if
current trends continue (IPCC, 2014).
The concept of global warming and the requirement to reduce carbon emissions
have prompted the formation of net-zero targets. Most nations throughout the
world are working toward net zero emissions as a solution to this issue, which
requires that the amount of greenhouse gas emissions produced and removed from
the atmosphere be equal (Council).
To counteract global warming, countries worldwide have committed to balancing
emitted greenhouse gases with removal strategies, acknowledging the urgent need
for collective action (Capitalist).
|
Figure 1;
|
Global
greenhouse gas emissions are increasing at a time when they should be
decreasing substantially. In 2022, global CO2 emissions from industrial
processes and energy combustion increased by 0.9%, or 321 Mt, reaching a new
record high of 36.8 Gt. ((IEA), 2022).
Almost three-quarters of emissions are created by energy consumption;
approximately one-fifth by agricultural and land use, which climbs to
one-quarter when the food system as a whole, including processing, packaging,
transport, and retail, and the remaining 8% by industry and waste. ((IEA), 2022). With the same case in Taiwan across all
sectors, the energy sector has long been the one accounting for the highest
total greenhouse gas emissions in Taiwan over the years. (Report(NIR), 2020). Greenhouse gas emissions (excluding LULUCF)
from the energy sector accounted for approximately 85.99% and 90.97% of total
emissions in 2005 and 2020, respectively, while the Industrial Process and
Product Use (IPPU) sector accounted for 10.12% and 6.94%, the agricultural
sector accounted for 1.37% and 1.17%, and the waste sector accounted for 2.52%
and 0.91%. (Report(NIR), 2020).
|
Figure 2;
|
|
Figure 3; ; |
Transitioning to
renewable energy sources such as solar energy has been one of the primary
options to achieve net zero emissions (Murdock et al., 2021). The usage of solar energy is
less prevalent in the other two main end-use industries, transportation and
heating. Climate mitigation initiatives, such as the 2015 Paris Climate
Agreement to limit global warming to no more than 1.5°C, are major policy
drivers for the deployment of renewable energy. Numerous analyses show how
extensive, potentially 100% renewable energy deployments can achieve the 1.5°C
limit (David S. Renné, 2022). In recent decades, the
production of electricity from solar farms with large-scale photovoltaic (PV)
systems has expanded dramatically, causing the capacity of all renewable energy
sources to increase every year since 2016. In fact, 83% of all new additions to
the power supply in 2020 came from renewable sources, with on-grid and off-grid
solar systems accounting for the biggest part (139 GW), followed by on-shore
and offshore wind generation (93 GW) (Murdock et al., 2021).
Solar energy is a
clean and safe energy source compared to fossil (Tsoutsos, Frantzeskaki, & Gekas, 2005). Solar panels at a solar farm
convert sunlight into electrical energy through a process known as photovoltaic
(PV) conversion. Solar energy produced by solar farms does not directly
generate any greenhouse gases, in contrast to fossil fuels, which when burned to
produce electricity emit greenhouse gases like carbon dioxide. Solar farms can
therefore provide electricity without increasing the total quantity of
greenhouse gas emissions associated with human activity, sometimes known as the
“carbon footprint.” (EAI, 2022).
|
Figure 4; |
Most of big
projects for solar farms installation requires a large-scale landscape (Chiabrando, Fabrizio, & Garnero, 2009). Landscape fragmentation, the elimination of existing flora and
fauna, changes in microclimate, and changes in surface albedo are some of the
main environmental impacts (Turney & Fthenakis, 2011). Furthermore, recent rapid
expansion in renewable energy has left environmental problems lagging behind (Lovich & Ennen, 2011). Although converting
ecological land into solar farms might have a negative impact on the
environment, it might also present an opportunity to create a more sustainable
future (Hernandez et al., 2014; ucsusa). By creating sustainable
energy, we can reduce our reliance on fossil fuels and mitigate the effects of
global warming. But it’s vital to carefully assess any potential environmental
implications of turning land into solar farms and come up with ways to lessen
any negative effects (Hernandez et al., 2014; ucsusa).
|
Figure 5;Coverting forest ecosystem into solar farms
|
1.2
Carbon, Water, and Energy Budget
The conversion
of forests into solar farms has implications for the carbon, water, and energy
budgets of the ecosystem (Sakai et al., 2004). Terrestrial ecosystems, and forests in
particular, play a significant role in the mitigation of global warming by
absorbing a sizeable portion of anthropogenic carbon dioxide emissions and
storing significant amounts of carbon in biomass and soils. (Le Quéré, 2018).
The balance between carbon uptake and release within the forest ecosystem is
known as the carbon budget of a forest (Haripriya, 2003). It is an indicator of how much carbon dioxide
(CO2) is taken up by the forest through photosynthesis and how much is released
back into the atmosphere through various processes (Pan et al., 2011).
·
Carbon Sequestration: During photosynthesis,
forests take up atmospheric CO2, using it to create biomass (trunks, branches,
leaves, etc.). This process is known as carbon sequestration. Trees serve as
carbon storage units, with their trunks and woody tissues storing a large
percentage of the absorbed carbon. (Lorenz & Lal, 2009).
·
Soil Organic Carbon: Forest soils also store
substantial organic carbon. Dead plant material, such as leaves, branches, and
roots, decompose in the soil, contributing to the soil’s organic carbon pool. (Mayer et al., 2020).
·
Respiration: Like all living organisms, trees
and other vegetation in forests undergo respiration, releasing CO2 back into
the atmosphere. This is a natural process where stored carbon is used for
energy production within plants. Respiration is a continuous process in
forests, occurring day and night, regardless of photosynthetic activity (ENERGY.GOV).
|
Figure 6; |
On the other hand, a forest’s water budget refers to the balance of
water inputs and outputs within the ecosystem (Rukundo & Doğan, 2019). It entails comprehending
variables including precipitation, evapotranspiration, runoff, and groundwater
recharge(Rukundo & Doğan, 2019). Forests play an important
part in the water cycle because they contribute to evapotranspiration, which is
the combined process of soil evaporation and plant transpiration (McDonnell & Pickett, 1990). While also the energy budget
of a forest relates to the energy exchanges that occur within the ecosystem(Bonan, 2008).
The sun’s energy is the primary input into the forest system, and the fraction
reflected is the albedo. A limited amount of energy is stored in the forest,
net thermal radiation and soil heat flow are responsible for some energy loss,
and the majority of energy is released by sensible and latent (or evaporative)
heat exchange with the atmosphere. Momentum transport in the surface boundary
layer, which rises with wind speed and surface roughness, facilitates heat
exchanges (McDonnell & Pickett, 1990).
|
Figure 7; |
|
Figure 8; ; |
The conversion of forest regions to solar farms alters the ecological
dynamics and environmental components (Suuronen, (2017)). Understanding the carbon, water, and energy
budgets when transitioning from forests to solar farms raises environmental
impact concerns since most of the environmental components, for example, the
direct carbon sequestration capacity of trees are lost. The management of water
cycles, local and regional hydrology, and rainfall patterns are all
significantly influenced by forests (Rukundo & Doğan, 2019). Compared to forests, solar
farms normally consume less water, but they do not actively support local water
cycling in the same way (Otterpohl, Grottker, & Lange, 1997). While solar farms directly
harness solar energy for the creation of electricity, forests absorb and use
solar energy through photosynthesis (Ramachandra & Shruthi, 2007). There is a lack of studies on
this subject in Taiwan and worldwide in general, and the existing studies
usually focus on the technical factors, resource measurement, and economic
impacts of installing solar power plants (Guney, 2016).
1.3
Eddy Covariance
Eddy covariance is a micro-meteorological method that
is currently popular to directly observe the exchanges of gas, energy, and
momentum between ecosystems and the atmosphere (Baldocchi, 2014). It measures the exchange of carbon, water,
methane, and energy between the earth’s surface and the atmosphere, empowering
researchers to advance their scientific understanding of climate and ecosystem
dynamics (Baldocchi, 2014). It provides information on the whole ecosystem
as it is capable of sampling a larger area (from a couple hundred m2
to a few km2), called the flux footprint. The method relies on
turbulent wind fluctuations, called ‘eddies’, which transport substances in the
corresponding parcels of air. These turbulent motions are sampled by the
instruments to determine the net movement of material across the
biosphere-atmosphere interface (Burba, 2013). Over a homogenous horizontal surface, energy and
gas fluxes in the vertical direction can be calculated as the covariance
between the vertical wind velocity and densities of measured quantities. A
positive flux denotes transport from the surface to the atmosphere and the
opposite is true for a negative flux. Continuous and automatic measuring at
fast sampling rates over extended time periods, results in large amounts of
data, that describe an ecosystem’s role in energy and carbon balance over a
range of time scales. The vertical turbulent fluxes
of carbon dioxide, water vapor, and heat are measured in solar farms using eddy
covariance systems made up of acoustic anemometers and gas analyzers (Burba, 2013). Using this equipment, it is possible to estimate the
exchange rates between the solar farm and the atmosphere by capturing
variations in wind speed, temperature, and gas concentrations at high
frequencies (Suuronen, (2017)).
By harnessing the insights gained from
studies employing eddy covariance in ecosystems to gauge carbon sequestration,
water use efficiency, and energy balance, we are poised to comprehensively
assess the carbon footprint of solar energy generation. This approach also
allows us to delve into the detailed interplay between environmental conditions,
water balances and energy production while critically evaluating the overall
efficiency of energy conversion processes (Suuronen, (2017))
2.
Research Objectives and Hypothesis
· Main objective: Assessing the
environmental impact of solar farms: An examination of carbon, water, and
energy budgets using the eddy covariance technique. Impact of land use change
from forest to solar park.
· Secondary objective: To examine
seasonal and diurnal variations in carbon, water, and energy balances to
understand the dynamic nature of the environmental impacts of solar farms.
· Proposed Hypothesis: In this study, we
expect that comparing solar farms with forest ecosystems using the eddy
covariance approach will provide insights into carbon, water, and energy flows.
The purpose of this hypothesis is to evaluate the environmental impact.
Carbon Budget Hypothesis:
Hypothesis 1: Solar farms will have significantly
lower net carbon uptake than forest ecosystems due to the lack of a photo
synthetically active canopy. (due to observation of low vegetation).
Water Budget Hypothesis:
Hypothesis 2: Solar farms will have a significantly
lower evapotranspiration rate compared to forests, resulting in a decreased
latent heat flux. This effect may be attributed to the reduced surface cover
and vegetation in solar farms.
Energy Budget Hypothesis:
Hypothesis 3: The diurnal energy partitioning pattern
in solar farms will differ from forests, with solar farms showing a higher
proportion of sensible heat flux, while forests will allocate more energy to
latent heat flux. This difference may be due to the contrasting surface
properties and canopy cover between the two ecosystems.
3.
Methodology
3.1
Research area
HUALIEN COUNTY
Hualien County is located
in the eastern coast of Taiwan Island. It
is Taiwan’s largest county by area but has one of the lowest populations in the
country due to its mountainous terrain. It has a humid subtropical climate with
warm and mild temperatures all year round. Summers are warm with average temperatures
of around 28 degrees Celsius, while winters are colder with average
temperatures of around 18 degrees Celsius. The region receives significant
rainfall, with the wettest months typically being from May to October and the
typhoon season, which can bring heavy rains and strong winds, particularly from
July to October. The humidity is particularly high in the summer months, which
increases the feeling of heat. Hualien has moderate sunshine, but this can vary
seasonally. The region has different seasons, with spring and autumn being the
most popular times for visitors due to milder weather and lower rainfall
(Central Weather Bureau, 2023) (Yung-Jaan, Shih-Chien, & Chiao-Chi, 2016).
The study focuses on two
sites: The FengLin-SengFeng Solar Farm (FLSF) and a forested area The DaNo-DaFu
(DNDF), located within the same geographical region.
3.1.1 FLSF
and DNDF (Hualien-Taiwan)
The FengLin-SengFeng Solar Farm (FLSF) is located in
FengLin-Township-Hualien, Taiwan. Founded in 2023 as a research facility, it is
equipped with a range of instruments that monitor the flow of carbon dioxide
(CO2), methane (CH4), and water vapor (H2O), in addition
to microclimate parameters. FLSF is located in a solar farm and provides a
unique investigative context. In addition, we include data from a plantation
forest to be able to answer the study questions on the topic of land use
change. This forest has the same characteristics as the original site forest
before part of it was converted into a solar farm. Located in the Guangfu
Township of Hualien County, the Danongdafu Forest is situated in the Eastern
Rift Valley, with the Central Mountain Range and the Coastal Mountain Range on
either side. The DaNo-DaFu (DNDF) site was founded in late 2016 in the
municipality of Guangfu-Hualien (Castro, 2019). Danongdafu was a sugar cane
plantation that ceased to be economically viable, leading the government to
undertake its conversion into a forest two decades ago.
3.1.2
EC Measurements
The forest eddy covariance tower has a height of 25 m and is
equipped with a range of instruments to monitor the CO2 and H2O flux as well as
the microclimate parameters of the ecosystem. In contrast to the plethora of
studies conducted in forested environments, there is a significant gap in our
understanding of the energy balance patterns specific to solar farms,
necessitating comprehensive exploration.
To bridge this gap, two data loggers were strategically
deployed at the FLSF solar farm: Smart Flux for Eddy and CR3000 Biomet for
microclimate assessments. These instruments enable continuous monitoring of
basic parameters such as temperature, relative humidity, and solar radiation.
In-depth investigations extend to subsurface layers, where two sets of sensors
are buried in situ. One set is positioned under solar panels while the other is
under an exposed corridor and exposed to direct sunlight. The EC system was
measured at 4.7 m above the ground at 10 Hz. The effects of rainfall are
carefully recorded by a strategically placed rain gauge in an exposed area.
This research archive includes not only the above-mentioned
atmospheric and soil parametors also valuable data on soil respiration obtained
through point sampling within the solar farm and the adjacent forest.
Additionally, Central Weather Bureau (CWB) data is included in the data set,
for the comparison purposes, adding broader context to the research effort.
|
Figure 9; |
3.2
Data acquisition and processing
3.2.1 Tower
and Sensor Information
Tower Location: 23.7969 N, 121.489 E, 114.6 m alt. (2023-04-18
based on Smart flux GPS)
Tower Height: 4.7 m by tape ruler measured in total with
concrete base (0.6 m) and stainless steel tower (4.1 m)
Solar Panel Height (above ground): 2.05 m
Radiation Pole Height: 3.10 m
Instruments Installed
|
Manufacturer – SN |
# @ Install. Height (m) |
Notes |
|
|
Barometric Pressure |
BP0611A |
1x @ 1.0 m |
by eyeballing estimated |
|
Temperature Humidity |
Vaisala HMP155 with Solar Shield DTR-500 |
1x @ 2.4 m |
|
|
Wind speed & direction |
RM Young 05305/05103 |
1x @ 5.0 m |
|
|
Radiation |
|||
|
-Net Radiation |
Hukseflux NR01 4-component net radiometer |
1x @ 3.1 m |
|
|
-PAR |
LICOR Quantum Sensor -190SB |
1x @ 3.1 m |
|
|
Soil |
|||
|
Soil Temperature / |
Acclima True TDR-315H |
under solar panel: under corridor: 1x @ -0.10 m |
|
|
Soil Heat Flux |
HukseFlux HFP01sc |
under solar panel: under corridor: 1x @ -0.10 m |
|
|
Eddy-Covariance Inst. |
|||
|
IRGA-CO2/H2O |
LICOR LI7500RS |
1x @ 4.95 m |
Northward separation: -3 cm Eastward separation: -7 cm Vertical separation: -5 cm |
|
IRGA-CH4 |
LICOR LI7700 |
1x @ 5.0 m |
Northward separation: +16.5 cm Eastward separation: -142.5 cm Vertical separation: 0 cm |
|
Sonic Anemometer |
Campbell Sci, CSAT3 |
1x @ 5.0 m |
North Offset: 130° |
|
Loggers |
|||
|
Biomet-Logger Box |
Campbell Sci, CR-1000X |
1x |
under solar pannel |
|
EC-Unit |
|
|
|
|
-LI-7550 Box |
LICOR |
1x, 1.2 m |
|
|
-smartflux |
LICOR |
1x |
inside LI-7550 Box |
|
Misc. |
|||
|
Water tank for LI7700 |
LICOR |
1x 1.7 m |
|
|
DC Power / Signal Junction Box |
|
1x, 1.2 m |
above tower base |
|
4G modem w/ switch hub |
ASUS |
1x |
inside Power/Signal Junction Box |
|
Battery |
70 Ah |
1x |
inside the battery box under the solar panel |
|
AC/DDC Power Box |
|
1x |
under solar panel |
|
Computer |
ECS_LIVA |
1x |
inside Power Box |
|
Figure 10; |
|
Figure 11; |
3.2.2 Parameters
Measured
The parameters examined and measured in this study cover
a comprehensive range of environmental factors and enable a holistic
understanding of the ecosystem dynamics under consideration. These parameters
include the concentrations of carbon dioxide (CO2), which provide
information about the composition of the atmosphere. Net ecosystem exchange
(NEE) of carbon, a key metric in the carbon cycle, is carefully examined along
with gross primary productivity (GPP), ecosystem respiration (Re), and carbon sequestration
rates, providing insight into the ecosystem carbon balance. In addition,
evapotranspiration rates, soil moisture content and precipitation are examined.
Energy flows are evaluated using sensible heat flow, latent heat flow, net
radiation and geothermal heat flow. Meteorological factors such as temperature,
humidity, wind speed and direction, solar radiation and atmospheric pressure
are also examined. This comprehensive approach provides a concise yet thorough
analysis of ecosystem dynamics.
3.2.3 Data
collection
The Smartflux data logger, a core hardware component, serves a
dual purpose by not only storing sensor data but also collecting information
from various loggers. The recorded data, called raw data, is acquired at high
frequency with 10 measurements per second, ensuring detailed temporal
resolution of the monitored parameters. Data was retrieved daily throughout the
research period from January to December 2023. In addition to real-time data,
previously collected information was integrated into the analysis.
Consequently, the study period encompasses the collected data and provides a
comprehensive representation of the environmental dynamics examined.
3.2.4 Data
processing
In the research data processing sequence, raw data from
different loggers were first combined to create a basic data set. Followed by
systematic calculations that generate 1-minute, 30-minute, and daily summary
files, making the data set more manageable for in-depth analysis. Briefly, the
LI-COR EddyPro® software (version 4.2.0, LICOR Biosciences, Lincoln, NE, USA)
was utilized to calculate the flow data. The EddyPro results were further
evaluated and data was flagged that did not meet the quality assurance/quality
control (QA/QC) criteria established for parameters such as instrument signal
strength (i.e. insufficient power), turbulence levels, and wind directions, a
careful recalculation emerges a large amount of data, ensuring precision in
flow measurements, which are crucial for environmental research. After
EddyPro’s recalculations, the dataset is refined using PyFluxPro, as shown in
(L1-L6). These procedures are designed to streamline and standardize critical
processes such as quality control, post-processing, gap filling, and data
partitioning received from flux towers. PyFluxPro works by converting flow
tower data into a finished, patchy product that splits Net Ecosystem Exchange
(NEE) into gross primary productivity (GPP) and ecosystem respiration (ER).
Finally, the recalculated and updated data are systematically summarized into
annual summaries that provide a comprehensive overview. This method allows for
meaningful interpretations consistent with the objectives of the study. The
entire data processing flow is methodically planned to generate meaningful
insights, thereby adding depth and reliability to current research initiatives.
3.2.5 Data
Analysis
The collected flux and weather data was thoroughly analyzed
using a variety of tools and techniques, including Microsoft Excel and R
Studio. These analytics platforms were critical in extracting key insights and
patterns from the dataset. Excel was used for early data exploration, cleaning,
and basic statistical analysis, whereas R Studio, a powerful statistical
computing environment, was used for more advanced statistical modeling and visualization.
The combination of these technologies ensured a complete analysis of the collected
weather data, and contributed to the depth and accuracy of our analysis.
4.
Results
4.1
Meteorological results
On the FLSF site, the graph
spanning January through December (2023) illustrates significant seasonal
changes in temperature and precipitation. The winter months (January to March)
experience lower values, with temperatures below 25 °C and rainfall below 100
mm, reflecting colder, drier conditions. However, in summer (June to August)
there is a significant increase, with temperatures ranging between 25 and 30 °C
and a maximum rainfall of 400 mm in September. This pattern highlights the
characteristic climate variations around the solar farm, emphasizing lower
temperatures and reduced precipitation in the winter and higher temperatures
with increased precipitation in the summer months.
|
Figure 12;
|
4.2
Temporal
Variation in Carbon, Water, and Energy Fluxes
4.3
Comparison
of Flux Measurements between Sites
|
|
4.4
Correlation
Analysis and Environmental Drivers
5.1
Interpretation
of Findings
5.2
Comparison
with Previous Studies
5.3
Limitations
and Future Research Directions
6.
Conclusion
- Summary
of Key Findings: - Recap
of the main findings regarding carbon, water, and energy fluxes in forest
and solar panel farm ecosystems. - Emphasis
on the significance of the study in advancing knowledge on land use
change impacts and renewable energy development. - Implications
for Policy and Practice: - Discussion
on the implications of the study findings for land management practices,
renewable energy policy, and climate change mitigation strategies.
7.
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