Pub Date : 2024-07-09DOI: 10.1088/2515-7620/ad5b3b
Devanil Choudhury, Debashis Nath, Wen Chen
The response of the Asian Summer Monsoon (ASM) circulation to the Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) forcing scenarios is examined using the CESM1 state-of-the-art global circulation model from 2021 to 2050. The projections show that monsoon precipitation will increase over East Asia, the North Pacific Ocean, the Indian Peninsula, and the Bay of Bengal under the RCP4.5 scenario. Conversely, the South Indian Ocean, West Asia, the Middle East, and the Central Pacific Ocean exhibit a decreasing trend in precipitation. Under the RCP8.5 scenario, precipitation is projected to increase over a wider swath of the Indian Ocean and the Middle East Asia. In the RCP4.5 scenario, the low-level wind circulation is likely to strengthen over the entire northern Indian Ocean, extending to the South China Sea, thereby increasing moisture transport from the Indian Ocean to peninsular India and the South China Sea. Conversely, in the RCP8.5 scenario, easterly winds strengthen over the South Indian Ocean, leading to an increase in moisture transport from the equatorial West Pacific Ocean to the Indian Ocean. A weak (strong) cyclonic circulation in response to the east-centered (west-centered) low sea level pressure trend over the North Pacific in RCP4.5 (RCP8.5) scenario is projected to help maintaining a strong (weak) ASM circulation from the India to east Asia. Internal climate variability is also calculated, revealing that the North Pacific Ocean near the Bering Sea is likely to play a dominating role and contribute significantly to the future ASM dynamics. In both scenarios, internal variability is found to substantially contribute to changes in monsoon circulation over the Indian Ocean.
{"title":"Asian summer monsoon responses under RCP4.5 and RCP8.5 scenarios in CESM large ensemble simulations","authors":"Devanil Choudhury, Debashis Nath, Wen Chen","doi":"10.1088/2515-7620/ad5b3b","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5b3b","url":null,"abstract":"The response of the Asian Summer Monsoon (ASM) circulation to the Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) forcing scenarios is examined using the CESM1 state-of-the-art global circulation model from 2021 to 2050. The projections show that monsoon precipitation will increase over East Asia, the North Pacific Ocean, the Indian Peninsula, and the Bay of Bengal under the RCP4.5 scenario. Conversely, the South Indian Ocean, West Asia, the Middle East, and the Central Pacific Ocean exhibit a decreasing trend in precipitation. Under the RCP8.5 scenario, precipitation is projected to increase over a wider swath of the Indian Ocean and the Middle East Asia. In the RCP4.5 scenario, the low-level wind circulation is likely to strengthen over the entire northern Indian Ocean, extending to the South China Sea, thereby increasing moisture transport from the Indian Ocean to peninsular India and the South China Sea. Conversely, in the RCP8.5 scenario, easterly winds strengthen over the South Indian Ocean, leading to an increase in moisture transport from the equatorial West Pacific Ocean to the Indian Ocean. A weak (strong) cyclonic circulation in response to the east-centered (west-centered) low sea level pressure trend over the North Pacific in RCP4.5 (RCP8.5) scenario is projected to help maintaining a strong (weak) ASM circulation from the India to east Asia. Internal climate variability is also calculated, revealing that the North Pacific Ocean near the Bering Sea is likely to play a dominating role and contribute significantly to the future ASM dynamics. In both scenarios, internal variability is found to substantially contribute to changes in monsoon circulation over the Indian Ocean.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"2016 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-08DOI: 10.1088/2515-7620/ad5ccd
Jiajin Wang, Jie Guo, Chunxue Wang, Yanmei Pang
In recent years, the Chengdu-Chongqing Economic Circle (CCEC) has experienced frequent heat events, significantly impacting labor productivity. The CCEC is an important economic growth pole in western China. Therefore, an in-depth study of the impact of heat stress on labor productivity holds great significance for climate change adaptation and enhancing economic efficiency. Based on the relationship between the wet-bulb globe temperature (WBGT) and labor productivity of different industries, the labor productivity loss caused by heat in the CCEC was estimated using the observation data of the meteorological station and the projection results of the BCC-CSM2-MR model from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results showed that the impact of heat on the labor productivity of different industries in the CCEC mainly occurs from June to August, with the largest impact on agriculture, followed by industry, and the smallest impact on service sectors. Losses from heat stress to labor productivity in agriculture, industry, and services showed a significant increasing trend from 1980 to 2020 but a decreasing trend in comprehensive labor productivity loss. From 2020–2100, labor productivity losses in different industries due to heat stress show an increasing and then decreasing trend in the low emissions scenario, productivity losses in the medium emissions scenario are characterized by an increasing and then sustained change, and labor productivity losses in the high emissions scenario show a sustained increasing trend from 2020. By the end of the 21st century, the increase in labor productivity losses across different industries under the high emission scenario is approximately 15%–23%, and the large value center shifts slightly to the west. In most areas, the losses of agricultural, industrial, service, and comprehensive labor productivity exceed 45%, 32%, 20%, and 24%, respectively.
{"title":"Impact of global warming on labor productivity in the Chengdu-Chongqing economic circle, China","authors":"Jiajin Wang, Jie Guo, Chunxue Wang, Yanmei Pang","doi":"10.1088/2515-7620/ad5ccd","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5ccd","url":null,"abstract":"In recent years, the Chengdu-Chongqing Economic Circle (CCEC) has experienced frequent heat events, significantly impacting labor productivity. The CCEC is an important economic growth pole in western China. Therefore, an in-depth study of the impact of heat stress on labor productivity holds great significance for climate change adaptation and enhancing economic efficiency. Based on the relationship between the wet-bulb globe temperature (WBGT) and labor productivity of different industries, the labor productivity loss caused by heat in the CCEC was estimated using the observation data of the meteorological station and the projection results of the BCC-CSM2-MR model from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results showed that the impact of heat on the labor productivity of different industries in the CCEC mainly occurs from June to August, with the largest impact on agriculture, followed by industry, and the smallest impact on service sectors. Losses from heat stress to labor productivity in agriculture, industry, and services showed a significant increasing trend from 1980 to 2020 but a decreasing trend in comprehensive labor productivity loss. From 2020–2100, labor productivity losses in different industries due to heat stress show an increasing and then decreasing trend in the low emissions scenario, productivity losses in the medium emissions scenario are characterized by an increasing and then sustained change, and labor productivity losses in the high emissions scenario show a sustained increasing trend from 2020. By the end of the 21st century, the increase in labor productivity losses across different industries under the high emission scenario is approximately 15%–23%, and the large value center shifts slightly to the west. In most areas, the losses of agricultural, industrial, service, and comprehensive labor productivity exceed 45%, 32%, 20%, and 24%, respectively.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"18 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-05DOI: 10.1088/2515-7620/ad5ad9
Jinge Zhang, Chunxiang Li, Tianbao Zhao
Predicting future mean precipitation poses significant challenges due to uncertainties among climate models, complicating water resource management. In this study, we introduce a novel methodology to mitigate uncertainty in future mean precipitation projections over China on a grid-by-grid basis. By constraining precipitation parameters of the Gamma distribution, we establish emergent constraints on parameters, revealing significant correlations between historical and future simulations. Our analysis spans the periods 2040–2069 and 2070–2099 under low-to-moderate and high emission scenarios. We observe reductions in uncertainty across most regions of China, with constrained mean precipitation indicating increases in monsoon regions and decreases in non-monsoon zones relative to raw projections. Notably, the observed 30%–40% increase in mean precipitation for the whole of China underscores the efficacy of our methodology. These observationally constrained results provide valuable insights into current precipitation projections, offering actionable information for water resource planning and climate adaptation strategies amidst future uncertainties.
{"title":"Qualifying uncertainty of precipitation projections over China: mitigating uncertainty with emergent constraints","authors":"Jinge Zhang, Chunxiang Li, Tianbao Zhao","doi":"10.1088/2515-7620/ad5ad9","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5ad9","url":null,"abstract":"Predicting future mean precipitation poses significant challenges due to uncertainties among climate models, complicating water resource management. In this study, we introduce a novel methodology to mitigate uncertainty in future mean precipitation projections over China on a grid-by-grid basis. By constraining precipitation parameters of the Gamma distribution, we establish emergent constraints on parameters, revealing significant correlations between historical and future simulations. Our analysis spans the periods 2040–2069 and 2070–2099 under low-to-moderate and high emission scenarios. We observe reductions in uncertainty across most regions of China, with constrained mean precipitation indicating increases in monsoon regions and decreases in non-monsoon zones relative to raw projections. Notably, the observed 30%–40% increase in mean precipitation for the whole of China underscores the efficacy of our methodology. These observationally constrained results provide valuable insights into current precipitation projections, offering actionable information for water resource planning and climate adaptation strategies amidst future uncertainties.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-03DOI: 10.1088/2515-7620/ad5b3d
Jenila Vincent M and Varalakshmi P
Extracting individual buildings from satellite images is crucial for various urban applications, including population estimation, urban planning, and other related fields. However, Extracting building footprints from remote sensing data is a challenging task because of scale differences, complex structures and different types of building. Addressing these issues, an approach that can efficiently detect buildings in images by generating a segmentation mask for each instance is proposed in this paper. This approach incorporates the Regional Convolutional Neural Network (MASK-RCNN), which combines Faster R-CNN for object mask prediction and boundary box recognition and was evaluated against other models like YOLOv5, YOLOv7 and YOLOv8 in a comparative study to assess its effectiveness. The findings of this study reveals that our proposed method achieved the highest accuracy in building extraction. Furthermore, we performed experiments on well-established datasets like WHU and INRIA, and our method consistently outperformed other existing methods, producing reliable results.
{"title":"Extraction of building footprint using MASK-RCNN for high resolution aerial imagery","authors":"Jenila Vincent M and Varalakshmi P","doi":"10.1088/2515-7620/ad5b3d","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5b3d","url":null,"abstract":"Extracting individual buildings from satellite images is crucial for various urban applications, including population estimation, urban planning, and other related fields. However, Extracting building footprints from remote sensing data is a challenging task because of scale differences, complex structures and different types of building. Addressing these issues, an approach that can efficiently detect buildings in images by generating a segmentation mask for each instance is proposed in this paper. This approach incorporates the Regional Convolutional Neural Network (MASK-RCNN), which combines Faster R-CNN for object mask prediction and boundary box recognition and was evaluated against other models like YOLOv5, YOLOv7 and YOLOv8 in a comparative study to assess its effectiveness. The findings of this study reveals that our proposed method achieved the highest accuracy in building extraction. Furthermore, we performed experiments on well-established datasets like WHU and INRIA, and our method consistently outperformed other existing methods, producing reliable results.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"67 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-03DOI: 10.1088/2515-7620/ad5b3f
Guneet Sandhu, Olaf Weber, Michael O Wood, Horatiu A Rus and Jason Thistlethwaite
Extant literature reveals limited examination of risk management strategies and tools to support decision-making for sustainable water management in the private sector in Ontario, Canada. Moreover, a gap persists in understanding how water risks are prioritized and managed in the private sector. Addressing these gaps, this transdisciplinary study applied a novel normative-analytical risk governance theoretical framework to water security risks, which combines analytical risk estimation with normative priorities and insights of practitioners, to examine contextually-attuned water risk management strategies and develop a decision-support tool. Using mixed methods, the study first employed a survey to elicit practitioner priorities for seven water risk indicators and investigated water risk management approaches. Then, interviews were conducted to obtain in-depth understanding about the priorities, strategies, opportunities, and role of trust in water risk management. The study found that a combination of regulatory, voluntary, and multi-stakeholder participatory approaches is needed, contingent on the severity of water risks, sector, location, and context. Moreover, the criteria of flexibility, efficiency, strategic incentives, and economic and regulatory signals, are essential. Finally, using secondary data analysis, the study integrated interdisciplinary risk data with practitioner priorities to develop a first-of-a-kind decision-support tool for water risk management in Ontario, ‘WATR-DST’. WATR-DST is an automated tool that applies the study’s findings and assists multi-sector water-related decisions, practices, and investments by providing contextually-attuned risk information in a user-friendly format. Based on the user inputs (location, sector, and source type), it displays the severity of seven water risks, qualitative themes under public and media attention, and recommends water risk management strategies. Thus, the study contributes to knowledge in sustainability management, risk analysis, and environmental management by demonstrating the novel application of the normative-analytical framework for water risk management in the private sector. WATR-DST is a key contribution envisioned to improve multi-sector water-related decisions in Ontario.
{"title":"Developing a transdisciplinary tool for water risk management and decision-support in Ontario, Canada","authors":"Guneet Sandhu, Olaf Weber, Michael O Wood, Horatiu A Rus and Jason Thistlethwaite","doi":"10.1088/2515-7620/ad5b3f","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5b3f","url":null,"abstract":"Extant literature reveals limited examination of risk management strategies and tools to support decision-making for sustainable water management in the private sector in Ontario, Canada. Moreover, a gap persists in understanding how water risks are prioritized and managed in the private sector. Addressing these gaps, this transdisciplinary study applied a novel normative-analytical risk governance theoretical framework to water security risks, which combines analytical risk estimation with normative priorities and insights of practitioners, to examine contextually-attuned water risk management strategies and develop a decision-support tool. Using mixed methods, the study first employed a survey to elicit practitioner priorities for seven water risk indicators and investigated water risk management approaches. Then, interviews were conducted to obtain in-depth understanding about the priorities, strategies, opportunities, and role of trust in water risk management. The study found that a combination of regulatory, voluntary, and multi-stakeholder participatory approaches is needed, contingent on the severity of water risks, sector, location, and context. Moreover, the criteria of flexibility, efficiency, strategic incentives, and economic and regulatory signals, are essential. Finally, using secondary data analysis, the study integrated interdisciplinary risk data with practitioner priorities to develop a first-of-a-kind decision-support tool for water risk management in Ontario, ‘WATR-DST’. WATR-DST is an automated tool that applies the study’s findings and assists multi-sector water-related decisions, practices, and investments by providing contextually-attuned risk information in a user-friendly format. Based on the user inputs (location, sector, and source type), it displays the severity of seven water risks, qualitative themes under public and media attention, and recommends water risk management strategies. Thus, the study contributes to knowledge in sustainability management, risk analysis, and environmental management by demonstrating the novel application of the normative-analytical framework for water risk management in the private sector. WATR-DST is a key contribution envisioned to improve multi-sector water-related decisions in Ontario.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wind power plays a vital role in the electricity generation of many countries, including Ethiopia. It serves as a valuable complement to hydropower during the dry season, and its affordability is crucial for the growth of industrial centers. However, accurately estimating wind energy poses significant challenges due to its random nature, severe variability, and dependence on wind speed. Numerous techniques have been employed to tackle this problem, and recent research has shown that Artificial Neural Network (ANN) models excel in prediction accuracy. This study aims to assess the effectiveness of different ANN network types in estimating the monthly average daily wind power at Adama Wind Farm I. The collected data was divided into three sets: training (70%), testing (15%), and validation (15%). Four network types, namely Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg–Marquardt (LR), were utilized with seven input parameters for prediction. The performance of these networks was evaluated using Mean Absolute Percentage Error (MAPE) and R-squared (R2). The EBP network type demonstrated exceptional performance in estimating wind power for all wind turbines in Groups GI, GII, and GIII. Additionally, all proposed network types achieved impressive accuracy levels with MAPE ranging from 0.0119 to 0.0489 and R2 values ranging from 0.982 to 0.9989. These results highlight the high predictive accuracy attained at the study site. Consequently, we can conclude that the ANN model’s network types were highly effective in predicting the monthly averaged daily wind power at Adama Wind Farm I. By leveraging the power of ANN models, this research contributes to improving wind energy estimation, thereby enabling more reliable and efficient utilization of wind resources. The findings of this study have practical implications for the wind energy industry and can guide decision-making processes regarding wind power generation and integration into the energy mix.
风能在包括埃塞俄比亚在内的许多国家的发电中发挥着至关重要的作用。在旱季，它是水力发电的重要补充，其经济性对工业中心的发展至关重要。然而，由于风能的随机性、严重的多变性和对风速的依赖性，准确估算风能面临着巨大挑战。为解决这一问题，人们采用了许多技术，最近的研究表明，人工神经网络（ANN）模型在预测准确性方面表现出色。本研究旨在评估不同类型的人工神经网络在估算 Adama 风电场 I 的月平均日风力发电量方面的有效性。收集的数据分为三组：训练（70%）、测试（15%）和验证（15%）。使用了四种网络类型，即前馈反向传播（FFBP）、级联前馈反向传播（CFBP）、误差反向传播（EBP）和 Levenberg-Marquardt （LR），并使用七个输入参数进行预测。使用平均绝对百分比误差 (MAPE) 和 R 平方 (R2) 对这些网络的性能进行了评估。EBP 网络类型在估算 GI、GII 和 GIII 组所有风力涡轮机的风功率时表现出了卓越的性能。此外，所有提议的网络类型都达到了令人印象深刻的精度水平，MAPE 在 0.0119 到 0.0489 之间，R2 值在 0.982 到 0.9989 之间。这些结果凸显了研究地点所达到的高预测精度。因此，我们可以得出结论，ANN 模型的网络类型在预测 Adama 风电场 I 的月平均日风力发电量方面非常有效。通过利用 ANN 模型的强大功能，本研究有助于改进风能估算，从而更可靠、更高效地利用风能资源。本研究的结果对风能产业具有实际意义，可指导有关风力发电和将风能纳入能源组合的决策过程。
{"title":"Unleashing the power of artificial neural networks: accurate estimation of monthly averaged daily wind power at Adama wind farm I, Ethiopia","authors":"Tegenu Argaw Woldegiyorgis, Natei Ermias Benti, Birhanu Asmerom Habtemicheal and Ashenafi Admasu Jembrie","doi":"10.1088/2515-7620/ad592f","DOIUrl":"https://doi.org/10.1088/2515-7620/ad592f","url":null,"abstract":"Wind power plays a vital role in the electricity generation of many countries, including Ethiopia. It serves as a valuable complement to hydropower during the dry season, and its affordability is crucial for the growth of industrial centers. However, accurately estimating wind energy poses significant challenges due to its random nature, severe variability, and dependence on wind speed. Numerous techniques have been employed to tackle this problem, and recent research has shown that Artificial Neural Network (ANN) models excel in prediction accuracy. This study aims to assess the effectiveness of different ANN network types in estimating the monthly average daily wind power at Adama Wind Farm I. The collected data was divided into three sets: training (70%), testing (15%), and validation (15%). Four network types, namely Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg–Marquardt (LR), were utilized with seven input parameters for prediction. The performance of these networks was evaluated using Mean Absolute Percentage Error (MAPE) and R-squared (R2). The EBP network type demonstrated exceptional performance in estimating wind power for all wind turbines in Groups GI, GII, and GIII. Additionally, all proposed network types achieved impressive accuracy levels with MAPE ranging from 0.0119 to 0.0489 and R2 values ranging from 0.982 to 0.9989. These results highlight the high predictive accuracy attained at the study site. Consequently, we can conclude that the ANN model’s network types were highly effective in predicting the monthly averaged daily wind power at Adama Wind Farm I. By leveraging the power of ANN models, this research contributes to improving wind energy estimation, thereby enabling more reliable and efficient utilization of wind resources. The findings of this study have practical implications for the wind energy industry and can guide decision-making processes regarding wind power generation and integration into the energy mix.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"7 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-02DOI: 10.1088/2515-7620/ad5ad8
J P Evans and H M Imran
Model evaluations are performed by comparing a modelled quantity with an observation of that quantity and any deviation from this observed quantity is considered an error. We know that all observing systems have uncertainties, and multiple observational products for the same quantity can provide equally plausible ‘truths’. Thus, model errors depend on the choice of observation used in the evaluation exercise. We propose a method that considers models to be indistinguishable from observations when they lie within the range of observations, and hence are not assigned any error. Errors are assigned when models are outside the observational range. Errors calculated in this way can be used within traditional statistics to calculate the Observation Range Adjusted (ORA) version of that statistic. The ORA statistics highlight the measurable errors of models, provide more robust model performance rankings, and identify areas of the model where further model development is likely to lead to consistent model improvements.
{"title":"The observation range adjusted method: a novel approach to accounting for observation uncertainty in model evaluation","authors":"J P Evans and H M Imran","doi":"10.1088/2515-7620/ad5ad8","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5ad8","url":null,"abstract":"Model evaluations are performed by comparing a modelled quantity with an observation of that quantity and any deviation from this observed quantity is considered an error. We know that all observing systems have uncertainties, and multiple observational products for the same quantity can provide equally plausible ‘truths’. Thus, model errors depend on the choice of observation used in the evaluation exercise. We propose a method that considers models to be indistinguishable from observations when they lie within the range of observations, and hence are not assigned any error. Errors are assigned when models are outside the observational range. Errors calculated in this way can be used within traditional statistics to calculate the Observation Range Adjusted (ORA) version of that statistic. The ORA statistics highlight the measurable errors of models, provide more robust model performance rankings, and identify areas of the model where further model development is likely to lead to consistent model improvements.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"33 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-02DOI: 10.1088/2515-7620/ad5b3c
James McCall, Brenda Beatty, Jake Janski, Kate Doubleday, Jordan Martin, Heidi Hartmann, Leroy J Walston and Jordan Macknick
As more land is being utilized for large-scale solar energy projects, there are increasing discussions from stakeholders on how to utilize land under solar panels to promote biodiversity. One path is to plant habitat beneficial to pollinators and other insects, but there have been few long-term studies that examine how different vegetation and seed mixes establish underneath solar panels. This study addresses a scientific gap to determine whether native pollinator seed mixes successfully establish over time under solar arrays using a systematic assessment of eight seed mixes planted at three utility-scale solar sites in Minnesota. We assess establishment with a percent native coverage metric, which is an assessment of native species observations compared to total observations during percent cover analyses in our vegetative test plots. The percent native coverage metric allows for a measurement of how the seed mix established and how the seed mix persists over time. The percent native coverage under and in between the solar photovoltaic (PV) arrays rose from 10% after one year of planting to 58% after three years across all sites, while the native coverage of the full sun control area rose from 9.6% to 70% under the same period, showing that native prairie and pollinator plants successfully established under the array, although to a lesser extent than in full sun conditions. Percent native coverage under the PV arrays rose 5- to 8-fold for each of the three sites from over the course of the study, while the coverage of weeds decreased for all three sites over the same period. Percent native coverage varied by seed mix over the project years, but every seed mix experienced a higher percent native coverage year after year under the PV arrays. Our results did not indicate a difference in establishment across placement within the array; the center, west, and east portions of the areas in between panels had similar establishment rates at two out of three sites, indicating that the same seed mix can be applied throughout the array. Out of 101 plant species seeded, we observed the establishment of 68 species in our vegetative test plots, and we detailed the top 20 observed species to inform future seed mix development. Based on these findings, native pollinator vegetation can establish over time at solar arrays, and it can be suitable for creating habitat at utility-scale solar sites.
{"title":"Little prairie under the panel: testing native pollinator habitat seed mix establishment at three utility-scale solar sites in Minnesota","authors":"James McCall, Brenda Beatty, Jake Janski, Kate Doubleday, Jordan Martin, Heidi Hartmann, Leroy J Walston and Jordan Macknick","doi":"10.1088/2515-7620/ad5b3c","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5b3c","url":null,"abstract":"As more land is being utilized for large-scale solar energy projects, there are increasing discussions from stakeholders on how to utilize land under solar panels to promote biodiversity. One path is to plant habitat beneficial to pollinators and other insects, but there have been few long-term studies that examine how different vegetation and seed mixes establish underneath solar panels. This study addresses a scientific gap to determine whether native pollinator seed mixes successfully establish over time under solar arrays using a systematic assessment of eight seed mixes planted at three utility-scale solar sites in Minnesota. We assess establishment with a percent native coverage metric, which is an assessment of native species observations compared to total observations during percent cover analyses in our vegetative test plots. The percent native coverage metric allows for a measurement of how the seed mix established and how the seed mix persists over time. The percent native coverage under and in between the solar photovoltaic (PV) arrays rose from 10% after one year of planting to 58% after three years across all sites, while the native coverage of the full sun control area rose from 9.6% to 70% under the same period, showing that native prairie and pollinator plants successfully established under the array, although to a lesser extent than in full sun conditions. Percent native coverage under the PV arrays rose 5- to 8-fold for each of the three sites from over the course of the study, while the coverage of weeds decreased for all three sites over the same period. Percent native coverage varied by seed mix over the project years, but every seed mix experienced a higher percent native coverage year after year under the PV arrays. Our results did not indicate a difference in establishment across placement within the array; the center, west, and east portions of the areas in between panels had similar establishment rates at two out of three sites, indicating that the same seed mix can be applied throughout the array. Out of 101 plant species seeded, we observed the establishment of 68 species in our vegetative test plots, and we detailed the top 20 observed species to inform future seed mix development. Based on these findings, native pollinator vegetation can establish over time at solar arrays, and it can be suitable for creating habitat at utility-scale solar sites.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"28 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141518682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-30DOI: 10.1088/2515-7620/ad5931
Catherine E Slavik, Daniel A Chapman, Hollie Smith, Michael Coughlan and Ellen Peters
Background. Wildfire smoke events are increasing in frequency and intensity due to climate change. Children are especially vulnerable to health effects even at moderate smoke levels. However, it is unclear how parents respond to Air Quality Indices (AQIs) frequently used by agencies to communicate air pollution health risks. Methods. In an experiment (3 × 2 × 2 factorial design), 2,100 parents were randomly assigned to view one of twelve adapted AQI infographics that varied by visual (table, line, gauge), index type (AQI [0-500], AQHI [1-11+]), and risk level (moderate, high). Participants were told to imagine encountering the infographic in a short-term exposure scenario. They reported worry about wildfire smoke, intentions to take risk-mitigating actions (e.g., air purifier use), and support for various exposure reduction policies. Subsequently, participants were told to imagine encountering the same infographic daily during a school week in a long-term exposure scenario and again reported worry, action intentions, and policy support. Results. Parents’ responses significantly differentiated between risk levels that both pose a threat to children’s health; worry and action intentions were much higher in the high-risk group than the moderate-risk group in both short-exposure (F = 748.68 p<.001; F = 411.59, p<.001) and long-exposure scenarios (F = 470.51, p<.001; F = 212.01, p<.001). However, in the short-exposure scenario, when shown the AQHI [1-11+] with either the line or gauge visuals, parents’ action intentions were more similar between moderate- and high-risk level groups (3-way interaction, F = 6.03, p = .002). Conclusions. These results suggest some index formats such as the AQHI—rather than the AQI—may better attune parents to moderate levels of wildfire smoke being dangerous to children’s health. Our research offers insights for agencies and officials seeking to improve current public education efforts during wildfire smoke events and speaks to the critical need to educate parents and help them act short-term and long-term to protect children’s health.
{"title":"Motivating parents to protect their children from wildfire smoke: the impact of air quality index infographics","authors":"Catherine E Slavik, Daniel A Chapman, Hollie Smith, Michael Coughlan and Ellen Peters","doi":"10.1088/2515-7620/ad5931","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5931","url":null,"abstract":"Background. Wildfire smoke events are increasing in frequency and intensity due to climate change. Children are especially vulnerable to health effects even at moderate smoke levels. However, it is unclear how parents respond to Air Quality Indices (AQIs) frequently used by agencies to communicate air pollution health risks. Methods. In an experiment (3 × 2 × 2 factorial design), 2,100 parents were randomly assigned to view one of twelve adapted AQI infographics that varied by visual (table, line, gauge), index type (AQI [0-500], AQHI [1-11+]), and risk level (moderate, high). Participants were told to imagine encountering the infographic in a short-term exposure scenario. They reported worry about wildfire smoke, intentions to take risk-mitigating actions (e.g., air purifier use), and support for various exposure reduction policies. Subsequently, participants were told to imagine encountering the same infographic daily during a school week in a long-term exposure scenario and again reported worry, action intentions, and policy support. Results. Parents’ responses significantly differentiated between risk levels that both pose a threat to children’s health; worry and action intentions were much higher in the high-risk group than the moderate-risk group in both short-exposure (F = 748.68 p<.001; F = 411.59, p<.001) and long-exposure scenarios (F = 470.51, p<.001; F = 212.01, p<.001). However, in the short-exposure scenario, when shown the AQHI [1-11+] with either the line or gauge visuals, parents’ action intentions were more similar between moderate- and high-risk level groups (3-way interaction, F = 6.03, p = .002). Conclusions. These results suggest some index formats such as the AQHI—rather than the AQI—may better attune parents to moderate levels of wildfire smoke being dangerous to children’s health. Our research offers insights for agencies and officials seeking to improve current public education efforts during wildfire smoke events and speaks to the critical need to educate parents and help them act short-term and long-term to protect children’s health.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"20 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141518683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-30DOI: 10.1088/2515-7620/ad59f3
Abreham Birhane Kassay, Abraham Woldemichael Tuhar, Mihret Dananto Ulsido and Markos Mathewos Godebo
Hawassa characterizes a typical developing city in Ethiopia, owning to rapid urban growth and demographic trends. The combined effect of climate change and urban expansion is increasing the challenge to the environment and the services it provides. Relating changing environments with urban water management (UWM) is required to build resilience in the urban environment. This research analyzed local climate change and urban growth and linked it to UWM. The historical period 1990–2021 of daily rainfall, temperature variables, four satellite imageries, and DEM were analyzed. Changes in rainfall (annual and daily maximum) and temperature (maximum and minimum) trends are detected and projected to 2051 using a statistical-based model. With geospatial techniques sub-watersheds are delineated, and the urban cover change is quantified. The trend detection result implies an upward trend of annual and daily maximum rainfalls however a significance is insufficient (p > 0.05) to associate it with climate change during the study period. Maximum and minimum temperatures change indicate a positive and significant trend. The forecasting result suggests an increment of both temperatures (0.5 °C–1.5 °C) to the projected period compared to historical scenario. The land cover analysis results show the built-up area changed from 11.6 km2 (7.2%) to 42.5 km2 (26.5%) during the historical period, where the rate varies spatially. The surface runoff increased by 30.7% in the urban watersheds. With a growth rate of 8.9% built-up, the urban area will cover 73.6 km2 (45.9%) for the predicted period. The research finding justifies the potential to reorganize the relationship between the spatial effect of climate change and urban growth on UWM. Considering distinct characteristics of urban watershed, exposure to flooding risk, access to water demand and resilient to climate change have spatial variation. Thus, a local-specific planning approach will support effective UWM and climate adaptation for sustainable city development.
{"title":"Statistical-based spatial analysis on urban water management under changing environments: a case study of Hawassa, Ethiopia","authors":"Abreham Birhane Kassay, Abraham Woldemichael Tuhar, Mihret Dananto Ulsido and Markos Mathewos Godebo","doi":"10.1088/2515-7620/ad59f3","DOIUrl":"https://doi.org/10.1088/2515-7620/ad59f3","url":null,"abstract":"Hawassa characterizes a typical developing city in Ethiopia, owning to rapid urban growth and demographic trends. The combined effect of climate change and urban expansion is increasing the challenge to the environment and the services it provides. Relating changing environments with urban water management (UWM) is required to build resilience in the urban environment. This research analyzed local climate change and urban growth and linked it to UWM. The historical period 1990–2021 of daily rainfall, temperature variables, four satellite imageries, and DEM were analyzed. Changes in rainfall (annual and daily maximum) and temperature (maximum and minimum) trends are detected and projected to 2051 using a statistical-based model. With geospatial techniques sub-watersheds are delineated, and the urban cover change is quantified. The trend detection result implies an upward trend of annual and daily maximum rainfalls however a significance is insufficient (p > 0.05) to associate it with climate change during the study period. Maximum and minimum temperatures change indicate a positive and significant trend. The forecasting result suggests an increment of both temperatures (0.5 °C–1.5 °C) to the projected period compared to historical scenario. The land cover analysis results show the built-up area changed from 11.6 km2 (7.2%) to 42.5 km2 (26.5%) during the historical period, where the rate varies spatially. The surface runoff increased by 30.7% in the urban watersheds. With a growth rate of 8.9% built-up, the urban area will cover 73.6 km2 (45.9%) for the predicted period. The research finding justifies the potential to reorganize the relationship between the spatial effect of climate change and urban growth on UWM. Considering distinct characteristics of urban watershed, exposure to flooding risk, access to water demand and resilient to climate change have spatial variation. Thus, a local-specific planning approach will support effective UWM and climate adaptation for sustainable city development.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"80 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141518684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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