{"id":21946,"date":"2024-04-18T04:58:53","date_gmt":"2024-04-18T04:58:53","guid":{"rendered":"https:\/\/www.writemyessays.app\/blog\/questions\/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\/"},"modified":"2024-04-18T04:58:53","modified_gmt":"2024-04-18T04:58:53","slug":"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","status":"publish","type":"questions","link":"https:\/\/www.writemyessays.app\/blog\/questions\/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\/","title":{"rendered":"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"},"content":{"rendered":"
\n

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. <\/span><\/p>\n


<\/span><\/p>\n

Department of Humanity and Environmental Studies,<\/span><\/p>\n

National Dong Hwa University<\/span><\/p>\n

Seminar Report 3<\/p>\n

Evaluating Carbon, Water, and Energy Budgets Using Eddy Covariance
\nTechnique in a Solar Panel Farm: Implications of Land Use Change from Forest to
\nSolar Farm<\/span><\/span><\/p>\n

Nsia
\nAsanterabi Ulomi\uff08<\/span>611155004\uff09<\/span><\/p>\n

Advisor:
\nProf. Shih-Chieh Chang<\/p>\n

June, 2024<\/span><\/b><\/p>\n<\/div>\n



\n<\/span><\/b><\/p>\n

\n

 <\/p>\n

      <\/span>Contents<\/p>\n

Introduction<\/span>. <\/span><\/span>2<\/span><\/a><\/span><\/p>\n

1.1<\/span>        <\/span><\/span>Background<\/span>. <\/span><\/span>2<\/span><\/a><\/span><\/p>\n

1.2<\/span>        <\/span><\/span>Carbon,
\nWater, and Energy Budget<\/span> <\/span><\/span>6<\/span><\/a><\/span><\/p>\n

1.3<\/span>        <\/span><\/span>Eddy
\nCovariance<\/span>. <\/span><\/span>9<\/span><\/a><\/span><\/p>\n

2.<\/span>      <\/span><\/span>Research
\nObjectives and Hypothesis<\/span>. <\/span><\/span>11<\/span><\/a><\/span><\/p>\n

3.<\/span>      <\/span><\/span>Methodology<\/span>. <\/span><\/span>12<\/span><\/a><\/span><\/p>\n

3.1<\/span>        <\/span><\/span>Research
\narea<\/span>. <\/span><\/span>12<\/span><\/a><\/span><\/p>\n

3.1.1<\/span>     <\/span><\/span>FLSF and DNDF (Hualien-Taiwan)<\/span> <\/span><\/span>12<\/span><\/a><\/span><\/p>\n

3.2<\/span>        <\/span><\/span>Data
\nacquisition and processing<\/span>. <\/span><\/span>14<\/span><\/a><\/span><\/p>\n

3.2.1<\/span>     <\/span><\/span>Tower and Sensor Information<\/span>. <\/span><\/span>14<\/span><\/a><\/span><\/p>\n

3.2.2<\/span>     <\/span><\/span>Parameters Measured<\/span>. <\/span><\/span>17<\/span><\/a><\/span><\/p>\n

3.2.3<\/span>     <\/span><\/span>Data collection<\/span>. <\/span><\/span>17<\/span><\/a><\/span><\/p>\n

3.2.4<\/span>     <\/span><\/span>Data processing<\/span>. <\/span><\/span>18<\/span><\/a><\/span><\/p>\n

3.2.5<\/span>     <\/span><\/span>Data Analysis<\/span>. <\/span><\/span>18<\/span><\/a><\/span><\/p>\n

4.<\/span>      <\/span><\/span>Results<\/span>. <\/span><\/span>19<\/span><\/a><\/span><\/p>\n

4.1<\/span>        <\/span><\/span>Meteorological
\nresults<\/span>. <\/span><\/span>19<\/span><\/a><\/span><\/p>\n

4.2<\/span>        <\/span><\/span>Temporal Variation in Carbon, Water, and Energy Fluxes<\/span>. <\/span><\/span>19<\/span><\/a><\/span><\/p>\n

Temporal Variation in Carbon, Water, and Energy Fluxes
\nNet Ecosystem Exchange (NEE) from both sites.<\/span> <\/span><\/span>19<\/span><\/a><\/span><\/p>\n

4.3<\/span>        <\/span><\/span>Comparison of Flux Measurements between Sites<\/span>. <\/span><\/span>22<\/span><\/a><\/span><\/p>\n

4.4<\/span>        <\/span><\/span>Correlation Analysis and Environmental Drivers<\/span>. <\/span><\/span>23<\/span><\/a><\/span><\/p>\n

5.<\/span>      <\/span><\/span>Discussion<\/span>. <\/span><\/span>24<\/span><\/a><\/span><\/p>\n

5.1<\/span>        <\/span><\/span>Interpretation of Findings<\/span>. <\/span><\/span>24<\/span><\/a><\/span><\/p>\n

5.2<\/span>        <\/span><\/span>Comparison with Previous Studies<\/span>. <\/span><\/span>24<\/span><\/a><\/span><\/p>\n

5.3<\/span>        <\/span><\/span>Limitations and Future Research Directions<\/span>. <\/span><\/span>24<\/span><\/a><\/span><\/p>\n

6.<\/span>      <\/span><\/span>Conclusion<\/span>. <\/span><\/span>24<\/span><\/a><\/span><\/p>\n

7.<\/span>      <\/span><\/span>References<\/span>. <\/span><\/span>26<\/span><\/a><\/span><\/p>\n

 <\/p>\n<\/div>\n



\n<\/span><\/b><\/p>\n

\n

<\/a><\/a> <\/span><\/span><\/a><\/h1>\n

Abstract<\/b><\/span><\/span><\/span><\/p>\n

The
\nconversion of forested land into solar panel farms represents a significant
\nland use change with potential implications for carbon sequestration, water
\ndynamics, and energy exchange. This study utilizes eddy covariance measurements
\nto evaluate the impacts of land use change from forest to solar farms on
\ncarbon, water, and energy budgets. Data collected from both the forest and
\nsolar panel farm sites are analyzed to understand the variations in flux
\ndynamics and assess the environmental implications of the transition. The
\nfindings provide insights into the effects of land use change on ecosystem
\nprocesses and inform sustainable land management practices in the context of
\nglobal warming mitigation. ***(NEED SOME WORK DONE)<\/span><\/span><\/span><\/p>\n

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Introduction<\/a><\/span><\/span><\/span><\/h1>\n

1.1         
\n<\/span><\/span><\/span>Background<\/a><\/span><\/span><\/h2>\n

Global warming is a critical issue that has become a
\nmajor concern worldwide. A range of environmental impacts, including sea level
\nrise, extreme weather events, and ecosystem changes, are expected to result
\nfrom human activities leading to global warming of 1.0\u00b0C above pre-industrial
\nlevels. Global warming would most likely exceed 1.5\u00b0C between 2030 and 2052 if
\ncurrent trends continue (IPCC, 2014)<\/span>.
\nThe concept of global warming and the requirement to reduce carbon emissions
\nhave prompted the formation of net-zero targets. Most nations throughout the
\nworld are working toward net zero emissions as a solution to this issue, which
\nrequires that the amount of greenhouse gas emissions produced and removed from
\nthe atmosphere be equal (Council)<\/span>.
\nTo counteract global warming, countries worldwide have committed to balancing
\nemitted greenhouse gases with removal strategies, acknowledging the urgent need
\nfor collective action (Capitalist)<\/span>.
\n<\/b><\/p>\n

\n\n\n\n
\n
\n

Figure 1<\/span>;
\n A screenshot from the application. The
\n yellow shaded area represents the uncertainty of the estimated 30-year
\n average associated with past climate data and future climate projections
\n and the orange line shows the likely estimate of when we will reach a
\n warming of 1.5\u00b0C. Both are from the IPCC report, \u2018Global warming of
\n 1.5\u00b0C\u2019. Copernicus Climate Change Service,<\/span> (ECMWF, 2020)<\/span><\/p>\n

 <\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

 <\/p>\n

        <\/span>Global
\ngreenhouse gas emissions are increasing at a time when they should be
\ndecreasing substantially. In 2022, global CO2 emissions from industrial
\nprocesses and energy combustion increased by 0.9%, or 321 Mt, reaching a new
\nrecord high of 36.8 Gt. ((IEA), 2022)<\/span>.
\nAlmost three-quarters of emissions are created by energy consumption;
\napproximately one-fifth by agricultural and land use, which climbs to
\none-quarter when the food system as a whole, including processing, packaging,
\ntransport, and retail, and the remaining 8% by industry and waste. ((IEA), 2022)<\/span>. <\/span>With the same case in Taiwan across all
\nsectors, the energy sector has long been the one accounting for the highest
\ntotal greenhouse gas emissions in Taiwan over the years. (Report(NIR), 2020)<\/span>. Greenhouse gas emissions (excluding LULUCF)
\nfrom the energy sector accounted for approximately 85.99% and 90.97% of total
\nemissions in 2005 and 2020, respectively, while the Industrial Process and
\nProduct Use (IPPU) sector accounted for 10.12% and 6.94%, the agricultural
\nsector accounted for 1.37% and 1.17%, and the waste sector accounted for 2.52%
\nand 0.91%. (Report(NIR), 2020)<\/span>. <\/p>\n

 <\/p>\n

\n\n\n\n
\n
\n

Figure 2<\/span>;
\n Global greenhouse gas emissions by sectors (EAI, 2022)<\/span><\/p>\n

 <\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

\n\n\n\n
\n
\n

Figure 3<\/span>;
\n 1990-2020 Trends in Greenhouse Gas
\n Emission by Sector in Taiwan<\/span> (Report(NIR), 2020)<\/span><\/p>\n

;<\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

 <\/p>\n

      <\/span>Transitioning to
\nrenewable energy sources such as solar energy has been one of the primary
\noptions to achieve net zero emissions (Murdock et al., 2021)<\/span>. The usage of solar energy is
\nless prevalent in the other two main end-use industries, transportation and
\nheating. Climate mitigation initiatives, such as the 2015 Paris Climate
\nAgreement to limit global warming to no more than 1.5\u00b0C, are major policy
\ndrivers for the deployment of renewable energy. Numerous analyses show how
\nextensive, potentially 100% renewable energy deployments can achieve the 1.5\u00b0C
\nlimit (David S. Renn\u00e9, 2022)<\/span>. In recent decades, the
\nproduction of electricity from solar farms with large-scale photovoltaic (PV)
\nsystems has expanded dramatically, causing the capacity of all renewable energy
\nsources to increase every year since 2016. In fact, 83% of all new additions to
\nthe power supply in 2020 came from renewable sources, with on-grid and off-grid
\nsolar systems accounting for the biggest part (139 GW), followed by on-shore
\nand offshore wind generation (93 GW) (Murdock et al., 2021)<\/span>. <\/p>\n

    <\/span>Solar energy is a
\nclean and safe energy source compared to fossil (Tsoutsos, Frantzeskaki, & Gekas, 2005)<\/span>. Solar panels at a solar farm
\nconvert sunlight into electrical energy through a process known as photovoltaic
\n(PV) conversion. Solar energy produced by solar farms does not directly
\ngenerate any greenhouse gases, in contrast to fossil fuels, which when burned to
\nproduce electricity emit greenhouse gases like carbon dioxide. Solar farms can
\ntherefore provide electricity without increasing the total quantity of
\ngreenhouse gas emissions associated with human activity, sometimes known as the
\n“carbon footprint.” (EAI, 2022)<\/span>.\n<\/p>\n

\n\n\n\n
\n
\n

Figure 4<\/span>;
\n An In-depth Comparison: Solar Energy vs.
\n Fossil Fuels<\/span> (EnergySage, 2021)<\/span><\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

 <\/p>\n

      <\/span>Most of big
\nprojects for solar farms installation requires a large-scale landscape (Chiabrando, Fabrizio, & Garnero, 2009)<\/span>. <\/b>Landscape fragmentation, the elimination of existing flora and
\nfauna, changes in microclimate, and changes in surface albedo are some of the
\nmain environmental impacts (Turney & Fthenakis, 2011)<\/span>. Furthermore, recent rapid
\nexpansion in renewable energy has left environmental problems lagging behind (Lovich & Ennen, 2011)<\/span>. Although converting
\necological land into solar farms might have a negative impact on the
\nenvironment, it might also present an opportunity to create a more sustainable
\nfuture (Hernandez et al., 2014; ucsusa)<\/span>. By creating sustainable
\nenergy, we can reduce our reliance on fossil fuels and mitigate the effects of
\nglobal warming. But it’s vital to carefully assess any potential environmental
\nimplications of turning land into solar farms and come up with ways to lessen
\nany negative effects (Hernandez et al., 2014; ucsusa)<\/span>.<\/p>\n

\n\n\n\n
\n
\n

Figure 5<\/span>;Coverting forest ecosystem into solar farms<\/span><\/p>\n

 <\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

  <\/span><\/p>\n

    <\/span><\/p>\n

 <\/h2>\n

1.2         
\n<\/span><\/span><\/span>Carbon, Water, and Energy Budget<\/a><\/h2>\n

       <\/span>The conversion
\nof forests into solar farms has implications for the carbon, water, and energy
\nbudgets of the ecosystem (Sakai et al., 2004)<\/span>. Terrestrial ecosystems, and forests in
\nparticular, play a significant role in the mitigation of global warming by
\nabsorbing a sizeable portion of anthropogenic carbon dioxide emissions and
\nstoring significant amounts of carbon in biomass and soils. (Le Qu\u00e9r\u00e9, 2018)<\/span>.
\nThe balance between carbon uptake and release within the forest ecosystem is
\nknown as the carbon budget of a forest (Haripriya, 2003)<\/span>. It is an indicator of how much carbon dioxide
\n(CO2) is taken up by the forest through photosynthesis and how much is released
\nback into the atmosphere through various processes (Pan et al., 2011)<\/span>.<\/p>\n

\u00b7      
\n<\/span><\/span><\/span>Carbon Sequestration: During photosynthesis,
\nforests take up atmospheric CO2, using it to create biomass (trunks, branches,
\nleaves, etc.). This process is known as carbon sequestration. Trees serve as
\ncarbon storage units, with their trunks and woody tissues storing a large
\npercentage of the absorbed carbon. (Lorenz & Lal, 2009)<\/span>.<\/p>\n

\u00b7      
\n<\/span><\/span><\/span>Soil Organic Carbon: Forest soils also store
\nsubstantial organic carbon. Dead plant material, such as leaves, branches, and
\nroots, decompose in the soil, contributing to the soil\u2019s organic carbon pool. (Mayer et al., 2020)<\/span>.<\/p>\n

\u00b7      
\n<\/span><\/span><\/span>Respiration: Like all living organisms, trees
\nand other vegetation in forests undergo respiration, releasing CO2 back into
\nthe atmosphere. This is a natural process where stored carbon is used for
\nenergy production within plants. Respiration is a continuous process in
\nforests, occurring day and night, regardless of photosynthetic activity (ENERGY.GOV)<\/span>.<\/p>\n

\n\n\n\n
\n
\n

Figure 6<\/span>;
\n Carbon budget of a forest (Pan et al., 2011)<\/span><\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

 <\/p>\n

    
\n<\/span>On the other hand, a forest’s water budget refers to the balance of
\nwater inputs and outputs within the ecosystem (Rukundo & Do\u011fan, 2019)<\/span>. It entails comprehending
\nvariables including precipitation, evapotranspiration, runoff, and groundwater
\nrecharge(Rukundo & Do\u011fan, 2019)<\/span>. Forests play an important
\npart in the water cycle because they contribute to evapotranspiration, which is
\nthe combined process of soil evaporation and plant transpiration (McDonnell & Pickett, 1990)<\/span>. While also the energy budget
\nof a forest relates to the energy exchanges that occur within the ecosystem(Bonan, 2008)<\/span>.
\nThe sun’s energy is the primary input into the forest system, and the fraction
\nreflected is the albedo. A limited amount of energy is stored in the forest,
\nnet thermal radiation and soil heat flow are responsible for some energy loss,
\nand the majority of energy is released by sensible and latent (or evaporative)
\nheat exchange with the atmosphere. Momentum transport in the surface boundary
\nlayer, which rises with wind speed and surface roughness, facilitates heat
\nexchanges (McDonnell & Pickett, 1990)<\/span>. <\/p>\n

\n\n\n\n
\n
\n

Figure 7<\/span>;
\n Components of the hydrological cycle and water budget of a forest
\n environment (Watkins, 2015)<\/span><\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

\n\n\n\n
\n
\n

Figure 8<\/span>;
\n Energy exchange of forest<\/span> (McDonnell & Pickett, 1990)<\/span><\/p>\n

;<\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

 <\/p>\n

    
\n<\/span>The conversion of forest regions to solar farms alters the ecological
\ndynamics and environmental components (Suuronen, (2017))<\/span>. Understanding the carbon, water, and energy
\nbudgets when transitioning from forests to solar farms raises environmental
\nimpact concerns since most of the environmental components, for example, the
\ndirect carbon sequestration capacity of trees are lost. The management of water
\ncycles, local and regional hydrology, and rainfall patterns are all
\nsignificantly influenced by forests (Rukundo & Do\u011fan, 2019)<\/span>. Compared to forests, solar
\nfarms normally consume less water, but they do not actively support local water
\ncycling in the same way (Otterpohl, Grottker, & Lange, 1997)<\/span>. While solar farms directly
\nharness solar energy for the creation of electricity, forests absorb and use
\nsolar energy through photosynthesis (Ramachandra & Shruthi, 2007)<\/span>. There is a lack of studies on
\nthis subject in Taiwan and worldwide in general, and the existing studies
\nusually focus on the technical factors, resource measurement, and economic
\nimpacts of installing solar power plants (Guney, 2016)<\/span>.\n<\/p>\n

1.3         
\n<\/span><\/span><\/span>Eddy Covariance<\/a><\/h2>\n

Eddy covariance is a micro-meteorological method that
\nis currently popular to directly observe the exchanges of gas, energy, and
\nmomentum between ecosystems and the atmosphere (Baldocchi, 2014)<\/span><\/span>. <\/span>It measures the exchange of carbon, water,
\nmethane, and energy between the earth\u2019s surface and the atmosphere, empowering
\nresearchers to advance their scientific understanding of climate and ecosystem
\ndynamics (Baldocchi, 2014)<\/span><\/span>. <\/span>It provides information on the whole ecosystem
\nas it is capable of sampling a larger area (from a couple hundred m2<\/sup>
\nto a few km2<\/sup>), called the flux footprint. The method relies on
\nturbulent wind fluctuations, called \u2018eddies\u2019, which transport substances in the
\ncorresponding parcels of air. These turbulent motions are sampled by the
\ninstruments to determine the net movement of material across the
\nbiosphere-atmosphere interface <\/span>(Burba, 2013)<\/span><\/span>. <\/span>Over a homogenous horizontal surface, energy and
\ngas fluxes in the vertical direction can be calculated as the covariance
\nbetween the vertical wind velocity and densities of measured quantities. A
\npositive flux denotes transport from the surface to the atmosphere and the
\nopposite is true for a negative flux. Continuous and automatic measuring at
\nfast sampling rates over extended time periods, results in large amounts of
\ndata, that describe an ecosystem\u2019s role in energy and carbon balance over a
\nrange of time scales. The vertical turbulent fluxes
\nof carbon dioxide, water vapor, and heat are measured in solar farms using eddy
\ncovariance systems made up of acoustic anemometers and gas analyzers <\/span>(Burba, 2013)<\/span><\/span>. Using this equipment, it is possible to estimate the
\nexchange rates between the solar farm and the atmosphere by capturing
\nvariations in wind speed, temperature, and gas concentrations at high
\nfrequencies  <\/span><\/span>(Suuronen, (2017))<\/span><\/span>. <\/span><\/p>\n

       <\/span>By harnessing the insights gained from
\nstudies employing eddy covariance in ecosystems to gauge carbon sequestration,
\nwater use efficiency, and energy balance, we are poised to comprehensively
\nassess the carbon footprint of solar energy generation. This approach also
\nallows us to delve into the detailed interplay between environmental conditions,
\nwater balances and energy production while critically evaluating the overall
\nefficiency of energy conversion processes <\/span>(Suuronen, (2017))<\/span><\/span><\/p>\n<\/div>\n



\n<\/span><\/p>\n

\n

2.       
\n<\/span><\/span><\/span>Research Objectives and Hypothesis<\/a><\/h1>\n

\u00b7       <\/span><\/span><\/span><\/span>Main objective: Assessing the
\nenvironmental impact of solar farms: An examination of carbon, water, and
\nenergy budgets using the eddy covariance technique. Impact of land use change
\nfrom forest to solar park. <\/span><\/span><\/p>\n

\u00b7       <\/span><\/span><\/span><\/span>Secondary objective: To examine
\nseasonal and diurnal variations in carbon, water, and energy balances to
\nunderstand the dynamic nature of the environmental impacts of solar farms. <\/span><\/span><\/p>\n

\u00b7       <\/span><\/span><\/span><\/span>Proposed Hypothesis: In this study, we
\nexpect that comparing solar farms with forest ecosystems using the eddy
\ncovariance approach will provide insights into carbon, water, and energy flows.
\nThe purpose of this hypothesis is to evaluate the environmental impact.<\/span><\/span><\/p>\n

Carbon Budget Hypothesis<\/b>:<\/p>\n

Hypothesis 1<\/i>: Solar farms will have significantly
\nlower net carbon uptake than forest ecosystems due to the lack of a photo
\nsynthetically active canopy. (due to observation of low vegetation).<\/p>\n

Water Budget Hypothesis<\/b>:<\/p>\n

Hypothesis 2<\/i>: Solar farms will have a significantly
\nlower evapotranspiration rate compared to forests, resulting in a decreased
\nlatent heat flux. This effect may be attributed to the reduced surface cover
\nand vegetation in solar farms.<\/p>\n

Energy Budget Hypothesis<\/b>:<\/p>\n

Hypothesis 3<\/i>: The diurnal energy partitioning pattern
\nin solar farms will differ from forests, with solar farms showing a higher
\nproportion of sensible heat flux, while forests will allocate more energy to
\nlatent heat flux. This difference may be due to the contrasting surface
\nproperties and canopy cover between the two ecosystems.<\/p>\n

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\n<\/span><\/b><\/p>\n

\n

3.       
\n<\/span><\/span><\/span>Methodology<\/a><\/h1>\n

3.1         
\n<\/span><\/span><\/span>Research area<\/a><\/h2>\n

HUALIEN COUNTY <\/p>\n

Hualien County is located
\nin the eastern coast of Taiwan Island. <\/span>It
\nis Taiwan’s largest county by area but has one of the lowest populations in the
\ncountry due to its mountainous terrain. It has a humid subtropical climate with
\nwarm and mild temperatures all year round. Summers are warm with average temperatures
\nof around 28 degrees Celsius, while winters are colder with average
\ntemperatures of around 18 degrees Celsius. The region receives significant
\nrainfall, with the wettest months typically being from May to October and the
\ntyphoon season, which can bring heavy rains and strong winds, particularly from
\nJuly to October. The humidity is particularly high in the summer months, which
\nincreases the feeling of heat. Hualien has moderate sunshine, but this can vary
\nseasonally. The region has different seasons, with spring and autumn being the
\nmost popular times for visitors due to milder weather and lower rainfall
\n(Central Weather Bureau, 2023) (Yung-Jaan, Shih-Chien, & Chiao-Chi, 2016).<\/p>\n

The study focuses on two
\nsites: The FengLin-SengFeng Solar Farm (FLSF) and a forested area The DaNo-DaFu
\n(DNDF), located within the same geographical region. <\/p>\n

3.1.1     <\/span><\/span><\/span>FLSF
\nand DNDF (Hualien-Taiwan)<\/a><\/h3>\n

The FengLin-SengFeng Solar Farm (FLSF) is located in
\nFengLin-Township-Hualien, Taiwan. Founded in 2023 as a research facility, it is
\nequipped with a range of instruments that monitor the flow of carbon dioxide
\n(CO2<\/sub>), methane (CH4), and water vapor (H2<\/sub>O), in addition
\nto microclimate parameters. FLSF is located in a solar farm and provides a
\nunique investigative context. In addition, we include data from a plantation
\nforest to be able to answer the study questions on the topic of land use
\nchange. This forest has the same characteristics as the original site forest
\nbefore part of it was converted into a solar farm. Located in the Guangfu
\nTownship of Hualien County, the Danongdafu Forest is situated in the Eastern
\nRift Valley, with the Central Mountain Range and the Coastal Mountain Range on
\neither side. The DaNo-DaFu (DNDF) site was founded in late 2016 in the
\nmunicipality of Guangfu-Hualien (Castro, 2019). Danongdafu was a sugar cane
\nplantation that ceased to be economically viable, leading the government to
\nundertake its conversion into a forest two decades ago. <\/p>\n

 <\/p>\n

3.1.2    
\n<\/span><\/span><\/span>EC Measurements<\/h3>\n

The forest eddy covariance tower has a height of 25 m and is
\nequipped with a range of instruments to monitor the CO2 and H2O flux as well as
\nthe microclimate parameters of the ecosystem. In contrast to the plethora of
\nstudies conducted in forested environments, there is a significant gap in our
\nunderstanding of the energy balance patterns specific to solar farms,
\nnecessitating comprehensive exploration.<\/p>\n

To bridge this gap, two data loggers were strategically
\ndeployed at the FLSF solar farm: Smart Flux for Eddy and CR3000 Biomet for
\nmicroclimate assessments. These instruments enable continuous monitoring of
\nbasic parameters such as temperature, relative humidity, and solar radiation.
\nIn-depth investigations extend to subsurface layers, where two sets of sensors
\nare buried in situ. One set is positioned under solar panels while the other is
\nunder an exposed corridor and exposed to direct sunlight. The EC system was
\nmeasured at 4.7 m above the ground at 10 Hz. The effects of rainfall are
\ncarefully recorded by a strategically placed rain gauge in an exposed area.<\/p>\n

This research archive includes not only the above-mentioned
\natmospheric and soil parametors also valuable data on soil respiration obtained
\nthrough point sampling within the solar farm and the adjacent forest.
\nAdditionally, Central Weather Bureau (CWB) data is included in the data set,
\nfor the comparison purposes, adding broader context to the research effort.<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

\n\n\n\n
\n
\n

Figure 9<\/span>;
\n Study Area representation from Taiwan\u2019s
\n map of the solar farm site at FengLin-SengFeng-Hualien<\/span> (Google earth n.d)<\/span><\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

 <\/p>\n

3.2         
\n<\/span><\/span><\/span>Data acquisition and processing<\/a><\/h2>\n

3.2.1     <\/span><\/span><\/span>Tower
\nand Sensor Information<\/a><\/h3>\n

Tower Location:  <\/span>23.7969 N, 121.489 E, 114.6 m alt. (2023-04-18
\nbased on Smart flux GPS)<\/p>\n

Tower Height: 4.7 m by tape ruler measured in total with
\nconcrete base (0.6 m) and stainless steel tower (4.1 m)<\/p>\n

Solar Panel Height (above ground): 2.05 m<\/p>\n

Radiation Pole Height: 3.10 m<\/p>\n

Instruments Installed<\/p>\n

<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Type<\/a><\/p>\n<\/td>\n

\n

Manufacturer – SN<\/span><\/p>\n<\/td>\n

\n

# @ Install. Height (m)<\/span><\/p>\n<\/td>\n

\n

Notes<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Barometric Pressure<\/span><\/p>\n<\/td>\n

\n

BP0611A<\/span><\/p>\n<\/td>\n

\n

1x @ 1.0 m<\/span><\/p>\n<\/td>\n

\n

by eyeballing estimated<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Temperature <\/span><\/p>\n

Humidity <\/span><\/p>\n<\/td>\n

\n

Vaisala HMP155 with Solar Shield DTR-500<\/span><\/p>\n<\/td>\n

\n

1x @ 2.4 m<\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Wind speed & direction<\/span><\/p>\n<\/td>\n

\n

RM Young 05305\/05103<\/span><\/p>\n<\/td>\n

\n

1x @ 5.0 m<\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Radiation<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span>-Net Radiation<\/span><\/p>\n<\/td>\n

\n

Hukseflux NR01 4-component net radiometer<\/span><\/p>\n<\/td>\n

\n

1x @ 3.1 m<\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n<\/tr>\n

\n

-PAR<\/span><\/p>\n<\/td>\n

\n

LICOR Quantum Sensor -190SB<\/span><\/p>\n<\/td>\n

\n

1x @ 3.1 m<\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Soil<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Soil Temperature \/

\n Soil Water Content<\/span><\/p>\n<\/td>\n

\n

Acclima True TDR-315H<\/span><\/p>\n<\/td>\n

\n

under solar panel:

\n 1x @ -0.10 m

\n

\n <\/span><\/p>\n

under corridor:<\/span><\/p>\n

1x @ -0.10 m<\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Soil Heat Flux<\/span><\/p>\n<\/td>\n

\n

HukseFlux HFP01sc<\/span><\/span><\/p>\n<\/td>\n

\n

under solar panel:

\n 1x @ -0.10 m

\n

\n <\/span><\/p>\n

under corridor:<\/span><\/p>\n

1x @ -0.10 m<\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Eddy-Covariance Inst.<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

IRGA-CO2\/H2O<\/span><\/p>\n<\/td>\n

\n

LICOR LI7500RS<\/span><\/p>\n<\/td>\n

\n

1x @ 4.95 m<\/span><\/p>\n<\/td>\n

\n

Northward separation: -3 cm<\/span><\/p>\n

Eastward separation: -7 cm<\/span><\/p>\n

Vertical separation: -5 cm<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

IRGA-CH4<\/span><\/p>\n<\/td>\n

\n

LICOR LI7700<\/span><\/p>\n<\/td>\n

\n

1x @ 5.0 m<\/span><\/p>\n<\/td>\n

\n

Northward separation: +16.5 cm<\/span><\/p>\n

Eastward separation: -142.5 cm<\/span><\/p>\n

Vertical separation: 0 cm<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Sonic Anemometer<\/span><\/p>\n<\/td>\n

\n

Campbell Sci, CSAT3<\/span><\/p>\n<\/td>\n

\n

1x @ 5.0 m<\/span><\/p>\n<\/td>\n

\n

North Offset: 130\u00b0<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Loggers<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Biomet-Logger Box<\/span><\/p>\n<\/td>\n

\n

Campbell Sci, CR-1000X<\/span><\/p>\n<\/td>\n

\n

1x<\/span><\/p>\n<\/td>\n

\n

under solar pannel<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

EC-Unit<\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n<\/tr>\n

\n

-LI-7550 Box<\/span><\/p>\n<\/td>\n

\n

LICOR
\n LI7550<\/span><\/p>\n<\/td>\n

\n

1x, 1.2 m<\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n<\/tr>\n

\n

-smartflux<\/span><\/p>\n<\/td>\n

\n

LICOR
\n Smartflux<\/span><\/p>\n<\/td>\n

\n

1x<\/span><\/p>\n<\/td>\n

\n

inside LI-7550 Box<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Misc.<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Water tank for LI7700<\/span><\/p>\n<\/td>\n

\n

LICOR<\/span><\/p>\n<\/td>\n

\n

1x 1.7 m<\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n<\/tr>\n

\n

DC Power \/ Signal Junction Box<\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n

\n

1x, 1.2 m<\/span><\/p>\n<\/td>\n

\n

above tower base<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

4G modem w\/ switch hub<\/span><\/p>\n<\/td>\n

\n

ASUS <\/span><\/p>\n<\/td>\n

\n

1x<\/span><\/p>\n<\/td>\n

\n

inside Power\/Signal Junction Box<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Battery<\/span><\/p>\n<\/td>\n

\n

70 Ah<\/span><\/p>\n<\/td>\n

\n

1x<\/span><\/p>\n<\/td>\n

\n

inside the battery box under the solar panel<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

AC\/DDC Power Box<\/span><\/p>\n<\/td>\n

<\/span><\/p>\n

 <\/span><\/p>\n<\/td>\n

\n

1x<\/span><\/p>\n<\/td>\n

\n

under solar panel<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Computer<\/span><\/p>\n<\/td>\n

\n

ECS_LIVA<\/span><\/p>\n<\/td>\n

\n

1x<\/span><\/p>\n<\/td>\n

\n

inside Power Box<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/span><\/p>\n

 <\/p>\n

\n\n\n\n
\n
\n

Figure 10<\/span>;
\n Eddy Covariance Tower with sensors
\n information at (Study area ) FengLin-SengFeng-Hualien.<\/span><\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

\n\n\n\n
\n
\n

Figure 11<\/span>;
\n Other sensors, Radiation Pole, Rain Gauge, Biomet Logger Box.<\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

3.2.2     <\/span><\/span><\/span>Parameters
\nMeasured<\/a><\/h3>\n

The parameters examined and measured in this study cover
\na comprehensive range of environmental factors and enable a holistic
\nunderstanding of the ecosystem dynamics under consideration. These parameters
\ninclude the concentrations of carbon dioxide (CO2<\/sub>), which provide
\ninformation about the composition of the atmosphere. Net ecosystem exchange
\n(NEE) of carbon, a key metric in the carbon cycle, is carefully examined along
\nwith gross primary productivity (GPP), ecosystem respiration (Re), and carbon sequestration
\nrates, providing insight into the ecosystem carbon balance. In addition,
\nevapotranspiration rates, soil moisture content and precipitation are examined.
\nEnergy flows are evaluated using sensible heat flow, latent heat flow, net
\nradiation and geothermal heat flow. Meteorological factors such as temperature,
\nhumidity, wind speed and direction, solar radiation and atmospheric pressure
\nare also examined. This comprehensive approach provides a concise yet thorough
\nanalysis of ecosystem dynamics.<\/p>\n

3.2.3     <\/span><\/span><\/span>Data
\ncollection<\/a><\/h3>\n

The Smartflux data logger, a core hardware component, serves a
\ndual purpose by not only storing sensor data but also collecting information
\nfrom various loggers. The recorded data, called raw data, is acquired at high
\nfrequency with 10 measurements per second, ensuring detailed temporal
\nresolution of the monitored parameters. Data was retrieved daily throughout the
\nresearch period from January to December 2023. In addition to real-time data,
\npreviously collected information was integrated into the analysis.
\nConsequently, the study period encompasses the collected data and provides a
\ncomprehensive representation of the environmental dynamics examined.<\/p>\n

3.2.4     <\/span><\/span><\/span>Data
\nprocessing<\/a><\/h3>\n

In the research data processing sequence, raw data from
\ndifferent loggers were first combined to create a basic data set. Followed by
\nsystematic calculations that generate 1-minute, 30-minute, and daily summary
\nfiles, making the data set more manageable for in-depth analysis. Briefly, the
\nLI-COR EddyPro\u00ae software (version 4.2.0, LICOR Biosciences, Lincoln, NE, USA)
\nwas utilized to calculate the flow data. The EddyPro results were further
\nevaluated and data was flagged that did not meet the quality assurance\/quality
\ncontrol (QA\/QC) criteria established for parameters such as instrument signal
\nstrength (i.e. insufficient power), turbulence levels, and wind directions, a
\ncareful recalculation emerges a large amount of data, ensuring precision in
\nflow measurements, which are crucial for environmental research. After
\nEddyPro’s recalculations, the dataset is refined using PyFluxPro, as shown in
\n(L1-L6). These procedures are designed to streamline and standardize critical
\nprocesses such as quality control, post-processing, gap filling, and data
\npartitioning received from flux towers. PyFluxPro works by converting flow
\ntower data into a finished, patchy product that splits Net Ecosystem Exchange
\n(NEE) into gross primary productivity (GPP) and ecosystem respiration (ER).
\nFinally, the recalculated and updated data are systematically summarized into
\nannual summaries that provide a comprehensive overview. This method allows for
\nmeaningful interpretations consistent with the objectives of the study. The
\nentire data processing flow is methodically planned to generate meaningful
\ninsights, thereby adding depth and reliability to current research initiatives.<\/p>\n

3.2.5     <\/span><\/span><\/span>Data
\nAnalysis<\/a><\/h3>\n

The collected flux and weather data was thoroughly analyzed
\nusing a variety of tools and techniques, including Microsoft Excel and R
\nStudio. These analytics platforms were critical in extracting key insights and
\npatterns from the dataset. Excel was used for early data exploration, cleaning,
\nand basic statistical analysis, whereas R Studio, a powerful statistical
\ncomputing environment, was used for more advanced statistical modeling and visualization.
\nThe combination of these technologies ensured a complete analysis of the collected
\nweather data, and contributed to the depth and accuracy of our analysis.<\/p>\n

4.       
\n<\/span><\/span><\/span>Results<\/a><\/h1>\n

4.1         
\n<\/span><\/span><\/span>Meteorological results<\/a><\/h2>\n

On the FLSF site, the graph
\nspanning January through December (2023) illustrates significant seasonal
\nchanges in temperature and precipitation. The winter months (January to March)
\nexperience lower values, with temperatures below 25 \u00b0C and rainfall below 100
\nmm, reflecting colder, drier conditions. However, in summer (June to August)
\nthere is a significant increase, with temperatures ranging between 25 and 30 \u00b0C
\nand a maximum rainfall of 400 mm in September. This pattern highlights the
\ncharacteristic climate variations around the solar farm, emphasizing lower
\ntemperatures and reduced precipitation in the winter and higher temperatures
\nwith increased precipitation in the summer months. <\/p>\n\n\n\n
\n
\n

Figure 12<\/span>;
\n Shows initial condition at the FLSF site (Seasonal temperature and
\n Precipitation in year (2023)<\/p>\n

 <\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

4.2         
\n<\/span><\/span><\/span>Temporal
\nVariation in Carbon, Water, and Energy Fluxes<\/span><\/a><\/h2>\n

<\/h2>\n

4.3         
\n<\/span><\/span><\/span>Comparison
\nof Flux Measurements between Sites<\/span><\/a><\/h2>\n

<\/p>\n\n\n\n
\n
\n

 <\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n

4.4         
\n<\/span><\/span><\/span>Correlation
\nAnalysis and Environmental Drivers<\/span><\/a><\/h2>\n


5.       
\n<\/span><\/span><\/span>Discussion<\/span><\/a>
<\/p>\n

\n

<\/span><\/h1>\n

5.1         
\n<\/span><\/span><\/span>Interpretation
\nof Findings<\/span><\/a><\/span><\/h2>\n

 <\/p>\n

5.2         
\n<\/span><\/span><\/span>Comparison
\nwith Previous Studies<\/span><\/a><\/span><\/h2>\n

 <\/p>\n

5.3         
\n<\/span><\/span><\/span>Limitations
\nand Future Research Directions<\/span><\/a><\/span><\/h2>\n

 <\/p>\n

 <\/p>\n

6.       
\n<\/span><\/span><\/span>Conclusion<\/span><\/a><\/span><\/h1>\n
    \n
  1. Summary
    \n of Key Findings:<\/b><\/li>\n