Estimate Greenhouse Gas Emissions Research Articles

Headwater stream ecosystem: an important source of greenhouse gases to the atmosphere

Although an increasing number of reports have revealed that rivers are important sources of greenhouse gases (GHGs), the magnitude and underlying mechanism of riverine GHG emissions are still poorly understood. The global extent of the headwater stream ecosystem may represent one of the important GHG emitters. A global database of GHG measurements from 595 rivers, indicated that the concentrations of riverine GHGs continually decrease as the stream order increases. Further analysis suggested that high GHG emissions from headwater streams (Strahler stream orders of 1 to 3) could be related to the low levels of dissolved oxygen, massive terrestrially derived carbon/nitrogen inputs and large gas exchange velocity. Through a combination of the predicted river surface areas and gas transfer velocities, we estimated that globally, the rivers emit approximately 6.6 (5.5–7.8) Pg CO2, 29.5 (19.6–37.3) Tg CH4, and 0.6 (0.2–0.9) Tg N2O per year, and totally emit 7.6 (6.1–9.1) CO2 equivalent into atmosphere per year. The headwater streams contribute 72.3%, 75.5%, and 77.2% of the global riverine CO2, CH4, and N2O emissions, respectively. This study presents a systematic estimation of GHG emissions from river ecosystems worldwide and highlights the dominant role played by headwater streams in GHG evasions from global rivers.

Carbon footprint of dermatologic surgery.

Healthcare is a potent emitter of greenhouse gases amounting up to 7% of total estimated greenhouse gas emissions (CO2 e) for Australia. Australia has one of the highest incidences of skin cancer in the world but there is a paucity of data on the ecological impact of skin cancer excision/dermatologic surgery. The authors reviewed the various impact inventories in order to perform a life cycle assessment of skin cancer excision. A total of 8641 tonnes of estimated CO2 e are produced from dermatologic surgery annually in Australia (6751 tonnes from private clinical rooms and 1890 tonnes from hospitals) and the waste generated contributes significantly to terrestrial ecotoxicity and acidification of land and water. Various means can be carried out to reduce this impact, ranging from simple behavioural changes to larger, policy changes.

Divergent Controls on Stream Greenhouse Gas Concentrations Across a Land-Use Gradient

Inland waters can be significant sources of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) to the atmosphere. However, considerable uncertainty remains in regional and global estimates of greenhouse gas (GHG) emissions from freshwater ecosystems, particularly streams. Controls on GHG production in streams, such as water chemistry and sediment characteristics, are also poorly understood. The main objective of this study was to quantify spatial and temporal variability in GHG concentrations in 20 streams across a landscape with considerable variation in land use and land cover in New England, USA. Stream water was consistently supersaturated in CO2, CH4, and N2O, suggesting that these small streams are sources of GHGs to the atmosphere in this landscape. Results show that concentrations of dissolved CO2, CH4 and N2O differed in their spatial and temporal patterns and in their relationship to stream chemistry. Both bivariate and multivariate analyses revealed a unique combination of predictor variables for each gas, suggesting variation in the landscape attributes and in-stream processes that control GHG concentrations. Although hydrologic conditions did not explain variation among sites, temporal patterns in GHG concentrations align with seasonal phenologies in flow and temperature. We developed a conceptual model based on these data that describes the spatial variability in GHG production from streams and that can elucidate the dominant controls on each gas. Developing an understanding of the factors controlling GHG dynamics in streams can help assess and predict how fluvial ecosystems will respond to changes in climate and land use and can be used to incorporate emissions from streams into regional and global GHG emission inventories.

Impacts of home cooking methods and appliances on the GHG emissions of food.

Food is widely acknowledged as a major contributor to climate change but estimates of food-related greenhouse gas (GHG) emissions frequently consider supply chain stages only up to the farm gate or regional distribution centres. Here we estimate GHG emissions associated with different cooking methods and appliances in the UK. Data on current cooking practices were collected through a survey with more than 700 respondents. Our results reveal that home cooking accounts for as much as 61% of total emissions associated with specific foods, and that this can be substantially reduced through alternative, readily available cooking practices.

Estimation of Greenhouse Gas Emission in Rice Paddy Soil Under Slow Released N Fertilizer Application: Annual Investigation

Intermittent drainage can reduce methane (CH4) emission from rice paddy soils, but nitrous oxide (N2O) emission can increase. We believe that the slow released N fertilizer can mitigate N2O emissions by reducing N lost to the environment. In this study, we tried to assess the influence of slow N fertilizer on effective greenhouse gas (GHG) reduction. We installed three different treatments, urea (U) treatment, controlled release fertilizer (CRF) treatment, and hairy vetch with urea (HV) treatment. The emission rates of CH4 and N2O were monitored using the closed chamber method during cropping and fallow season. The grain yield was investigated to calculate yield scaled greenhouse gas intensity (GHGI). Compared with U treatment, CH4 emission was reduced in CRF but increased in HV treatment. In contrast, N2O emission was increased in CRF but reduced in HV treatment. Grain yield was increased in CRF and HV treatment than U treatment. The GHGI was the lowest in CRF treatment by high grain yield and low GHG emission. In contrast, GHGI was the highest in HV treatment due to increased CH4 emission. In conclusion, controlled release fertilizer can effectively reduce GHG emission. However, CRF application increased N2O emissions during the fallow season, and further investigation is needed to determine whether this is due to the effect of fertilizer residues. In addition, due to field experiments that are easy to influenced by the environmental condition, it seems necessary to verify the research results through additional investigations over many years. Grain yield, total GWP and GHGI during annual season in rice paddy under difference type of nitrogen fertilizer application.

Improving robustness of LCA results through stakeholder engagement: A case study of emerging oil sands technologies

Life cycle assessments can help inform decision-making about greenhouse gas (GHG) emission reduction opportunities but are often not embraced by stakeholders associated with industries where study results are highly scrutinized and often contentious. This project was motivated by stakeholder interest in understanding open source life cycle models (the Oil Production Greenhouse Gas Emissions Estimator, OPGEE, and the Petroleum Refinery Life Cycle Inventory Model, PRELIM) and how accurately they can estimate emissions for existing oil sands projects and emerging technologies. We evaluate the robustness of these models and improve them using data from three existing oil sands projects (mining + upgrading, mining + dilution, and steam assisted gravity drainage, SAGD, + dilution). The models are then applied to estimate the GHG emissions reduction potential for two emerging in situ oil sands technologies. We find that, when boundaries are aligned, OPGEE can generate upstream GHG emissions estimates for the projects modeled within 1-4% of company reported GHG emissions data. Extending the boundary to include indirect (life cycle) emissions can lead to a doubling in upstream GHG emissions intensity. The two emerging technologies evaluated in the study can reduce upstream emissions by 14-19% compared to a SAGD project operating at the same reservoir, or 1.4-1.9% on a well-to-wheel basis. This work contributes a revised process of conducting LCAs that includes stakeholder input throughout and results in more robust and transparent estimates of emissions from deploying existing and emerging technologies.

Estimation of Tunisian Greenhouse Gas Emissions from Different Livestock Species

This study aimed to determine the emissions of methane (CH4) and nitrous oxide (N2O) from seven Tunisian livestock species and their evolution over eleven consecutive years (2008–2018). The species of animals used were cattle (dairy and others), sheep, goats, camelids, horses, donkeys and mules, and poultry. The estimations of CH4 and N2O emissions were based on the Intergovernmental Panel on Climate Change (IPCC) guidelines for national inventories, using Tier 1 and Tier 2 approaches, with its default emission factors (EFs). The Tier 2 approach was applied only for the calculation of EF to estimate CH4 emissions related to livestock manure management. CH4 emission represented more than 92% of the total greenhouse gas (GHG) from livestock emissions. Moreover, 53% of the total CH4 emissions from livestock were derived from cattle, followed by sheep, goats, other mammals (camelids, horses, mules, and donkeys), and poultry. During the period covered by the study (2008–2018), a slight and continuous decrease of both livestock population and total GHG emissions was observed, mainly in terms of CH4. In mammals, CH4 emissions were greater than N2O emissions, whereas in poultry, N2O emissions were up to 2.6 times greater than CH4 emissions. The aggressive drive of the government to increase cattle and sheep production might affect CH4 emissions in the future. Therefore, periodic estimations of GHG emissions from livestock are required to follow the time trends for more rational decision-making regarding livestock and GHG emissions policies.

Cross-country analysis of life cycle assessment–based greenhouse gas emissions for automotive parts: Evaluation of coefficient of country

Life cycle assessments (LCAs) are time consuming and can be expensive. Sometimes a company simply needs to have a basic estimate of the environmental impacts of the country which they are conducting the operations. If a company plans to move a current established production line or set up a new production line, which countries should be considered for a full LCA? It would be useful to have a coefficient for each country which could scale an established LCA result to estimate each new country's emissions. The following study develops country coefficients which could be used to scale LCA results. After careful study of many environmental parameters, two were selected to develop the country coefficient: total primary energy supply and electricity grid mix. This coefficient was designed for fiber-reinforced composites; and as the sources of natural fiber could be very different; we also included forestry and agriculture in our study. At the end of the study, we performed LCA-based greenhouse gas emission estimates for several countries and compared the results to the estimates using the country coefficients. We were able to reach an impressive result of up to 95% accuracy in comparison to the full LCA In just simple steps.

Improving the estimation of greenhouse gas emissions from the Chinese coal-to-electricity chain by a bottom-up approach

Accurately estimating greenhouse gas (GHG) emissions from the Chinese coal-to-electricity chain is critical to meet the global goal of carbon reduction, but the efforts in previous work based on statistical data were not ideal. Here, we ingeniously proposed an improved estimation method from a bottom-up perspective, integrating ten technical indicators affecting point-source GHG emissions from coal mines, coal transport routes, and coal power units. This method quantifies provincial GHG emission factors of the coal-to-electricity chain in 2016, varying greatly within 964-1232 g CO2eq / kWh, and explains for the first time how the micro technical indicators affect macro carbon emission levels. Our estimates have a higher spatial resolution and lower uncertainty than existing studies, which can well represent the status quo in different provinces. Based on these emission factors, the total GHG emissions from the Chinese the coal-to-electricity chain in 2016 are estimated at 4232 Mt CO2eq and being spatially intensive in northern and eastern China. They are expected to peak in 2025 according to current policy but in advance by 2019 if transformation is accelerated. The variation province by province suggests that carbon reduction strategies should be tailored to the local specific situation and overall coordinated across the country.

Uncertainty analysis of stack gas flow measurements with an S-type Pitot tube for estimating greenhouse gas emissions using a continuous emission monitoring system

In order to reduce greenhouse gas (GHG) emissions from producers in the energy and industrial fields to mitigate climate change more effectively, accurate and reliable studies of methods capable of estimating GHG emissions with proper uncertainty levels should initially be carried out. Continuous emission monitoring systems (CEMS) directly measure GHG emissions by measuring the GHG concentrations and volumetric flow rates of flue gases in smokestacks. Most studies of CEMS have tended to focus on the accuracy and uncertainty levels associated with GHG concentrations rather than on volumetric flow measurements. Particularly, the uncertainty associated with volumetric gas flow measurements by CEMS at a smokestack considering the individual plant must be determined to reduce the overall uncertainty. In the present study, the uncertainties of stack gas flow measurements with an S-type Pitot tube to estimate GHG emissions by CEMS are evaluated by applying the GUM and Monte Carlo methods to an on-site smokestack of an energy power plant. Velocity measurements in the stack were conducted using an S-type Pitot tube mainly used in Korea. The mathematical model equations of the accumulated volumetric gas flowrate measurements for estimating GHG emissions by CEMS are expressed in the form of detailed uncertainty equations by GUM. In the uncertainty evaluations by GUM, the combined standard uncertainty equation is expressed with the average gas flow velocity, stack diameter, static pressure, temperature and water content. The relative expanded uncertainty for volumetric gas flowrate measurements with an S-type Pitot tube in a smokestack by GUM is estimated to be 3.81% for a coverage factor (k = 2) at a confidence level of approximately 95%. In addition, the Monte Carlo method was applied to evaluate the associated uncertainty of volumetric gas flowrate measurements by an S-type Pitot tube for comparison with the results by the GUM method. The results of the uncertainty evaluations by GUM and MCM show good agreement, with only a 0.05% difference in the uncertainty outcomes.

Estimating greenhouse gas emissions from direct land use change due to crop production in multiple countries

Greenhouse gas (GHG) emissions from direct land use change (LUC) in GHG footprint studies of crops are often estimated using national land use change statistics, as in many cases the exact location of crop cultivation and land use history is unknown. As such, these studies neglect spatial variability in land use change (amount and configuration) at the sub-national level as well as spatial variability in natural carbon stocks. For this reason, a spatial approach that enables consistent implementation of LUC emissions of crop production at different locations is developed and applied in this study. The dataset of crop production covers 69 crops cultivated on 1885 farms in 33 countries, spanning North and South America, Asia, Australia and Oceania, Europe and Africa, in the year 2014. Of the 1885 farms, 33% (619 farms) were identified to have LUC emissions when estimated at the local scale. LUC emissions of farms, derived using local scale location information, were found to have little correlation with those estimated at coarser spatial scales (such as the province or country level) using the spatial approach in this study or estimated using accounting approaches based on national statistics. Analysis at coarser spatial scales typically overestimated the LUC emissions of crops, as LUC in other regions can heavily influence these estimates. Therefore, it is concluded that local scale LUC emissions better represent local LUC dynamics, thereby improving the reliability of GHG footprint studies.

Improving the accuracy of electricity carbon footprint: Estimation of hydroelectric reservoir greenhouse gas emissions

Hydropower is usually considered as a low-carbon electricity source, as it does not lead to direct greenhouse gas (GHG) emissions, unlike producing electricity from fossil fuels. However, the flooding of lands following the construction of the dam generally leads to an increase in biogenic GHG emissions due to the degradation of biomass found in the newly created reservoir. The life cycle assessment (LCA) methodology is widely used to calculate and compare the carbon footprint of different electricity production pathways, while considering all life cycle stages. Net biogenic GHG emissions from hydropower reservoirs have been poorly considered in LCA because of the scarcity of data. These emissions are complex to quantify as several mechanisms are involved, and extrapolating observations from one reservoir to another is risky as emissions vary greatly depending on different parameters, such as climate, geographic location, age of impoundment, and watershed properties. The objective of this article is to compare different approaches to estimate hydropower reservoir emissions in LCA, to select the most appropriate one, and to apply it to the calculation of the carbon footprint of electricity distributed in the Canadian province of Québec. Net biogenic GHG emissions of all hydropower reservoirs in the province (with 2.5 and 97.5% confidence intervals), as estimated using the G-res model, are 16.5 (14.7–18.6) gCO2∙kWh−1 and 0.29 (0.23–0.35) gCH4∙kWh−1. Combined to ecoinvent data for other life cycle emissions, the carbon footprint of electricity distributed in the province in 2017 is 34.5 gCO2eq∙kWh−1.

Identifying and addressing knowledge gaps for improving greenhouse gas emissions estimates from tropical peat forest fires

Tropical peatlands are areas of high carbon density that are important in biosphere-atmosphere interactions. Drainage and burning of tropical peatlands releases about 5% of global greenhouse gas (GHG) emissions, yet there is great uncertainty in these estimates. Our comprehensive literature review of parameters required to calculate GHG emissions from burnt peat forests, following the international guidelines, revealed many gaps in knowledge of carbon pools and few recent supporting studies. To improve future estimates of the total ecosystem carbon balance and peatfire emissions this study aimed to account for all carbon pools: aboveground, deadwood, pyrogenic carbon (PyC) and peat of single and repeatedly burnt peat forests. A further aim was to identify the minimum sampling intensity required to detect with 80% power significant differences in these carbon pools among long unburnt, recently burnt and repeatedly burnt peat swamp forests.About 90 Mg C ha−1 remains aboveground as deadwood after a single fire and half of this remains after a second fire. One fire produces 4.5 ± 0.6 Mg C ha−1 of PyC, with a second fire increasing this to 7.1 ± 0.8 Mg C ha−1. For peat swamp forests these aboveground carbon pools are rarely accounted in estimates of emissions following multiple fires, while PyC has not been included in the total peat carbon mass balance. Peat bulk density and peat carbon content change with fire frequency, yet these parameters often remain constant in the published emission estimates following a single and multiple fires. Our power analysis indicated that as few as 12 plots are required to detect meaningful differences between fire treatments for the major carbon pools. Further field studies directed at improving the parameters for calculating carbon balance of disturbed peat forest ecosystems are required to better constrain peatfire GHG emission estimates.

Biogas from Agri-Food and Agricultural Waste Can Appreciate Agro-Ecosystem Services: The Case Study of Emilia Romagna Region

Agro-ecosystems are intensively exploited environments which are both providers and consumers of ecosystem services. The improvement of both provisioning and regulating services in cultivated landscapes is crucial for the sustainable development of rural areas. Among the provisioning services offered, producing biogas from the anaerobic digestion of residual biomass is nowadays a promising option for decreasing greenhouse gas (GHG) emissions, while avoiding the land use conflicts related to the use of dedicated crops. Based on the available quantitative data at a regional level, provisioning and regulating services provided by the use of agri-food waste, livestock waste and agricultural residues were assessed for the case of Emilia Romagna region, the second biggest biogas producer in Italy. One provisioning service, i.e., bioenergy generation, and three regulating services were considered: (i) air quality improvement by the reduction of odors derived from direct use of waste, (ii) regulation of soil nutrients by reducing organic load and digestate spreading, and (iii) global climate regulation by saving GHG emissions. A potential further generation of 52.7 MW electric power was estimated at the regional level. Digestate spreading on fields may reduce odor impact by more than 90%, while containing a higher percentage of inorganic nitrogen, which is readily available to plants. The estimated GHG emission savings were equal to 2,862,533 Mg CO2eq/yr, mainly due to avoided landfilling for agri-waste and avoided replacing of mineral fertilizers for livestock waste and agricultural residues. The results suggest that bioenergy generation from lignocellulosic, livestock and agro-industrial residues may improve some regulating services in agro-ecosystems, while helping to reach renewable energy targets, thus contributing to overcoming the provisioning vs. regulating services paradigm in human-managed ecosystems.

A web application to estimate the carbon footprint of constructed wetlands

Constructed wetlands (CWs) are engineering systems recognized as an efficient and sustainable option to wastewater treatment. Due to the growing interest in CWs for waste management, the number of works analyzing their footprint and impact has risen in the last years. Thus, the study of these systems and their components in construction, operation, and demolition phases is important to characterize the technology and achieve a fully environmental-friendly approach. Until now, no complete tools for measuring both direct and indirect greenhouse gas (GHG) emissions in CWs have been reported in the field. Some efforts in this line are Life Cycle Assessment tools, which can be economically expensive and usually require specific training. Therefore, this work aims to present a web application as an open and complete tool for the estimation of GHG emissions in CWs, including both direct and indirect emissions and considering all the stages involved in their management.

Assessment of Planetary Boundary Layer parametrizations and urban heat island comparison: Impacts and implications for tracer transport.

Accurate simulation of planetary boundary layer height (PBLH) is key to greenhouse gas emission estimation, air quality prediction and weather forecasting. This manuscript describes an extensive performance assessment of several Weather Research and Forecasting (WRF) model configurations where novel observations from ceilometers, surface stations and a flux tower were used to study their ability to reproduce planetary boundary layer heights (PBLH) and the impact that the urban heat island (UHI) has on the modeled PBLHs in the greater Washington, D.C. area. In addition, CO2 measurements at two urban towers were compared to tracer transport simulations. The ensemble of models used 4 PBL parameterizations, 2 sources of initial and boundary conditions and 1 configuration including the building energy parameterization (BEP) urban canopy model. Results have shown low biases over the whole domain and period for wind speed, wind direction and temperature with no drastic differences between meteorological drivers. We find that PBLH errors are mostly positively correlated with sensible heat flux errors, and that modeled positive UHI intensities are associated with deeper modeled PBLs over the urban areas. In addition, we find that modeled PBLHs are typically biased low during nighttime for most of the configurations with the exception of those using the MYNN parametrization and that these biases directly translate to tracer biases. Overall, the configurations using MYNN scheme performed the best, reproducing the PBLH and CO2 molar fractions reasonably well during all hours, thus opening the door to future nighttime inverse modeling.

Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.

Users’ Experiences with the Use of Transaction Data to Estimate Consumption-Based Emissions in a Carbon Calculator

With global greenhouse gas (GHG) emissions ever increasing, we are currently seeing a renewed interest in carbon footprint calculators (or carbon calculators for short). While carbon calculators have traditionally calculated emissions based on user input about e.g., food, heating, and traveling, a new development in this area is the use of transaction data to also estimate emissions based on consumption. Such carbon calculators should be able to provide users with more accurate estimations, easier input possibilities, and an incentive to continue using them. In this paper, we present the results from a survey sent to the users of such a novel carbon calculator, called Svalna. Svalna offers users the possibility to connect their bank account. The transaction data are then coupled with Environmental Extended Multi Regional Input Output data (EE-MRIO) for Swedish conditions which are used to determine a continuous overview of the user’s greenhouse gas emissions from consumption. The aim of the survey was to (a) understand whether people are willing to connect their bank account, (b) whether they trust the calculations of their emissions, and (c) whether they think the use of Svalna has an effect on their behaviour. Furthermore, we wanted to know how Svalna could be improved. While the results of the survey showed that many users were willing to connect their bank account, a rather large part of the users perceived safety risks in doing so. The users also showed an only average level of trust in the correctness of the estimated greenhouse gas emissions. A lack of trust was attributed to experiencing technical problems but also to not knowing how the emissions were calculated and because the calculator could not capture all details of the user’s life. However, many users still indicated that the use of Svalna had helped them to initiate action to reduce their emissions. In order to improve Svalna, the users wanted to be able to provide more details, e.g., by scanning receipts and get better options for dealing with a shared economy. We conclude this paper by discussing some opportunities and challenges for the use of transaction data in carbon footprint calculators.

FoodEx2 enhances the harmonization of food consumption, composition and carbon footprint databases

Abstract Introduction FoodEx2 is a food classification and description system developed and maintained by the European Food Safety Authority (EFSA). It is currently used at global level with the support of the Food and Agriculture Organization (FAO) of the United Nations and the World Health Organization (WHO) to harmonize datasets in the FAO/WHO Global Individual Food consumption data Tool - FAO/WHO GIFT. This is an open access online platform hosted by FAO and supported by WHO, for collation and dissemination of individual quantitative food consumption data, especially from low- and middle-income countries. Objective To demonstrate FoodEx2's usefulness in global harmonization of food description in food consumption datasets and its potential in facilitating food matching for various types of food data. Methodology Information was gathered from data owners of food consumption datasets and others who were trained to use FoodEx2, and on the number of datasets that have initiated or completed their data harmonization process, as a result of the FAO/WHO/EFSA collaboration. Main Findings Over 50 data owners of food consumption data and/or food composition tables (FCT) have been trained in data re-categorization with FoodEx2. As a result, 6 FCT and 25 food consumption datasets from 18 countries were or are being coded with FoodEx2, and out of those, 30 datasets are or will soon be disseminated through FAO/WHO GIFT in the form of infographics and microdata. Additionally, at least 3 databases of greenhouse gas emission (GHGE) estimates of foods have been mapped with FoodEx2. Conclusions Increased harmonization with FoodEx2 would enhance the consistency and reliability of nutrient intake assessments. Harmonization with FoodEx2 is being used to assess the environmental impact of diets and help identifying sustainable food consumption patterns.

High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex

We describe a new high spatial resolution surface water classification dataset generated for the Xingu river, Brazil, from its confluence with the Iriri river to the Pimental dam prior to construction of the Belo Monte hydropower complex, and after its operationalization. This river is well-known for its exceptionally high diversity and endemism in ichthyofauna. Pre-existing datasets generated from moderate resolution satellite imagery (e.g., 30 m) do not adequately capture the extent of the river. Accurate measurements of water extent are important for a range of applications utilizing surface water data, including greenhouse gas emission estimation, land cover change mapping, and habitat loss/change estimates, among others. We generated the new classifications from RapidEye imagery (5 m pixel size) for 2011 and PlanteScope imagery (3 m pixel size) for 2019 using a Geographic Object Based Image Analysis (GEOBIA) approach.