Daria Gushchina, Maria Tarasova, Elizaveta Satosina, Irina Zheleznova, Ekaterina Emelianova, Ravil Gibadullin, Alexander Osipov, Alexander Olchev
Forest ecosystems in the mid-latitudes of the Northern Hemisphere are significantly affected by frequent extreme weather events. How different forest ecosystems respond to these changes is a major challenge. This study aims to assess differences in the response of daily net ecosystem exchange (NEE) of CO2 and latent heat flux (LE) between different boreal and temperate ecosystems and the atmosphere to extreme weather events (e.g., anomalous temperature and precipitation). In order to achieve the main objective of our study, we used available reanalysis data and existing information on turbulent atmospheric fluxes and meteorological parameters from the global and regional FLUXNET databases. The analysis of NEE and LE responses to high/low temperature and precipitation revealed a large diversity of flux responses in temperate and boreal forests, mainly related to forest type, geographic location, regional climate conditions, and plant species composition. During the warm and cold seasons, the extremely high temperatures usually lead to increased CO2 release in all forest types, with the largest response in coniferous forests. The decreasing air temperatures that occur during the warm season mostly lead to higher CO2 uptake, indicating more favorable conditions for photosynthesis at relatively low summer temperatures. The extremely low temperatures in the cold season are not accompanied by significant NEE anomalies. The response of LE to temperature variations does not change significantly throughout the year, with higher temperatures leading to LE increases and lower temperatures leading to LE reductions. The immediate response to heavy precipitation is an increase in CO2 release and a decrease in evaporation. The cumulative effect of heavy precipitations is opposite to the immediate effect in the warm season and results in increased CO2 uptake due to intensified photosynthesis in living plants under sufficient soil moisture conditions.
{"title":"The Response of Daily Carbon Dioxide and Water Vapor Fluxes to Temperature and Precipitation Extremes in Temperate and Boreal Forests","authors":"Daria Gushchina, Maria Tarasova, Elizaveta Satosina, Irina Zheleznova, Ekaterina Emelianova, Ravil Gibadullin, Alexander Osipov, Alexander Olchev","doi":"10.3390/cli11100206","DOIUrl":"https://doi.org/10.3390/cli11100206","url":null,"abstract":"Forest ecosystems in the mid-latitudes of the Northern Hemisphere are significantly affected by frequent extreme weather events. How different forest ecosystems respond to these changes is a major challenge. This study aims to assess differences in the response of daily net ecosystem exchange (NEE) of CO2 and latent heat flux (LE) between different boreal and temperate ecosystems and the atmosphere to extreme weather events (e.g., anomalous temperature and precipitation). In order to achieve the main objective of our study, we used available reanalysis data and existing information on turbulent atmospheric fluxes and meteorological parameters from the global and regional FLUXNET databases. The analysis of NEE and LE responses to high/low temperature and precipitation revealed a large diversity of flux responses in temperate and boreal forests, mainly related to forest type, geographic location, regional climate conditions, and plant species composition. During the warm and cold seasons, the extremely high temperatures usually lead to increased CO2 release in all forest types, with the largest response in coniferous forests. The decreasing air temperatures that occur during the warm season mostly lead to higher CO2 uptake, indicating more favorable conditions for photosynthesis at relatively low summer temperatures. The extremely low temperatures in the cold season are not accompanied by significant NEE anomalies. The response of LE to temperature variations does not change significantly throughout the year, with higher temperatures leading to LE increases and lower temperatures leading to LE reductions. The immediate response to heavy precipitation is an increase in CO2 release and a decrease in evaporation. The cumulative effect of heavy precipitations is opposite to the immediate effect in the warm season and results in increased CO2 uptake due to intensified photosynthesis in living plants under sufficient soil moisture conditions.","PeriodicalId":37615,"journal":{"name":"Climate","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136012559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Callum F. Thompson, Charles Jones, Leila Carvalho, Anna T. Trugman, Donald D. Lucas, Daisuke Seto, Kevin Varga
Surface winds over California can compound fire risk during autumn, yet their long-term trends in the face of decadal warming are less clear compared to other climate variables like temperature, drought, and snowmelt. To determine where and how surface winds are changing most, this article uses multiple reanalyses and Remote Automated Weather Stations (RAWS) to calculate autumn 10 m wind speed trends during 1979–2020. Reanalysis trends show statistically significant increases in autumn night-time easterlies on the western slopes of the Sierra Nevada. Although downslope windstorms are frequent to this region, trends instead appear to result from elevated gradients in warming between California and the interior continent. The result is a sharper horizontal temperature gradient over the Sierra crest and adjacent free atmosphere above the foothills, strengthening the climatological nocturnal katabatic wind. While RAWS records show broad agreement, their trend is likely influenced by year-to-year changes in the number of observations.
{"title":"Autumn Surface Wind Trends over California during 1979–2020","authors":"Callum F. Thompson, Charles Jones, Leila Carvalho, Anna T. Trugman, Donald D. Lucas, Daisuke Seto, Kevin Varga","doi":"10.3390/cli11100207","DOIUrl":"https://doi.org/10.3390/cli11100207","url":null,"abstract":"Surface winds over California can compound fire risk during autumn, yet their long-term trends in the face of decadal warming are less clear compared to other climate variables like temperature, drought, and snowmelt. To determine where and how surface winds are changing most, this article uses multiple reanalyses and Remote Automated Weather Stations (RAWS) to calculate autumn 10 m wind speed trends during 1979–2020. Reanalysis trends show statistically significant increases in autumn night-time easterlies on the western slopes of the Sierra Nevada. Although downslope windstorms are frequent to this region, trends instead appear to result from elevated gradients in warming between California and the interior continent. The result is a sharper horizontal temperature gradient over the Sierra crest and adjacent free atmosphere above the foothills, strengthening the climatological nocturnal katabatic wind. While RAWS records show broad agreement, their trend is likely influenced by year-to-year changes in the number of observations.","PeriodicalId":37615,"journal":{"name":"Climate","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135967681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yadanar Kyaw, Thi Phuoc Lai Nguyen, Ekbordin Winijkul, Wenchao Xue, Salvatore G. P. Virdis
Agriculture, entwined with climatic conditions, plays a pivotal role in Thailand’s sustenance and economy. This study aimed to examine the trends of climate variability and its correlation with crop yields and social and farm factors affecting farm net income in Chiang Mai province, Thailand. Time series climate data (2002–2020) on temperature and rainfall and yields were analyzed using the Mann–Kendall trend test and Sen’s slope estimation to investigate the trends and their changes. The Pearson correlation was used to assess the association between climate variability and cultivated crop yields, and multiple linear regression was used to detect the factors influencing the farm net income. The findings show that the total annual rainfall showed an unchanged trend, but the annual temperature increased over time. Higher temperature negatively impacted longan yield but positively affected maize, with no significant impact on rice yield. The rainfall trend had no effect on crop yields. Despite declining trends in some cultivated crops’ yield, farm net income was unaffected by individual crop types. Farm income relied on cumulative output and geographic location. This research emphasizes the need for integrating climate data and forecasting models considering agronomic and socio-economic factors and crop suitability assessments for specific regions into adaptation policies and practice.
{"title":"The Effect of Climate Variability on Cultivated Crops’ Yield and Farm Income in Chiang Mai Province, Thailand","authors":"Yadanar Kyaw, Thi Phuoc Lai Nguyen, Ekbordin Winijkul, Wenchao Xue, Salvatore G. P. Virdis","doi":"10.3390/cli11100204","DOIUrl":"https://doi.org/10.3390/cli11100204","url":null,"abstract":"Agriculture, entwined with climatic conditions, plays a pivotal role in Thailand’s sustenance and economy. This study aimed to examine the trends of climate variability and its correlation with crop yields and social and farm factors affecting farm net income in Chiang Mai province, Thailand. Time series climate data (2002–2020) on temperature and rainfall and yields were analyzed using the Mann–Kendall trend test and Sen’s slope estimation to investigate the trends and their changes. The Pearson correlation was used to assess the association between climate variability and cultivated crop yields, and multiple linear regression was used to detect the factors influencing the farm net income. The findings show that the total annual rainfall showed an unchanged trend, but the annual temperature increased over time. Higher temperature negatively impacted longan yield but positively affected maize, with no significant impact on rice yield. The rainfall trend had no effect on crop yields. Despite declining trends in some cultivated crops’ yield, farm net income was unaffected by individual crop types. Farm income relied on cumulative output and geographic location. This research emphasizes the need for integrating climate data and forecasting models considering agronomic and socio-economic factors and crop suitability assessments for specific regions into adaptation policies and practice.","PeriodicalId":37615,"journal":{"name":"Climate","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136210965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zili Xiong, Wei Shangguan, Vahid Nourani, Qingliang Li, Xingjie Lu, Lu Li, Feini Huang, Ye Zhang, Wenye Sun, Hua Yuan, Xueyan Li
Land carbon fluxes play a critical role in ecosystems, and acquiring a comprehensive global database of carbon fluxes is essential for understanding the Earth’s carbon cycle. The primary methods of obtaining the spatial distribution of land carbon fluxes include utilizing machine learning models based on in situ measurements, estimating through satellite remote sensing, and simulating ecosystem models. Recently, an innovative machine learning product known as the Global Carbon Flux Dataset (GCFD) has been released. In this study, we assessed the reliability of the GCFD by comparing it with existing data products, including two machine learning products (FLUXCOM and NIES (National Institute for Environmental Studies)), two ecosystem model products (TRENDY and EC-LUE (eddy covariance–light use efficiency model)), and one remote sensing product (Global Land Surface Satellite), on both site and global scales. Our findings indicate that, in terms of average absolute difference, the spatial distribution of the GCFD is most similar to the NIES product, albeit with slightly larger discrepancies compared to the other two types of products. When using site observations as the benchmark, gross primary production (GPP), respiration of ecosystem (RECO), and net ecosystem exchange of machine learning products exhibit higher R2 (ranging from 0.57 to 0.85, 0.53–0.79, and 0.31–0.70, respectively) compared to model products and remote sensing products. Furthermore, we analyzed the spatial and temporal distribution characteristics of carbon fluxes in various regions. The results demonstrate an upward trend in both GPP and RECO over the past two decades, while NEE exhibits an opposite trend. This trend is particularly pronounced in tropical regions, where higher GPP is observed in tropical, subtropical, and oceanic climate zones. Additionally, two remote sensing variables that influence changes in carbon fluxes, i.e., fraction absorbed photosynthetically active radiation and leaf area index, exhibit relatively consistent spatial and temporal characteristics. Overall, our study can provide valuable insights into different types of carbon flux products and contribute to understanding the general features of global carbon fluxes.
{"title":"Assessing the Reliability of Global Carbon Flux Dataset Compared to Existing Datasets and Their Spatiotemporal Characteristics","authors":"Zili Xiong, Wei Shangguan, Vahid Nourani, Qingliang Li, Xingjie Lu, Lu Li, Feini Huang, Ye Zhang, Wenye Sun, Hua Yuan, Xueyan Li","doi":"10.3390/cli11100205","DOIUrl":"https://doi.org/10.3390/cli11100205","url":null,"abstract":"Land carbon fluxes play a critical role in ecosystems, and acquiring a comprehensive global database of carbon fluxes is essential for understanding the Earth’s carbon cycle. The primary methods of obtaining the spatial distribution of land carbon fluxes include utilizing machine learning models based on in situ measurements, estimating through satellite remote sensing, and simulating ecosystem models. Recently, an innovative machine learning product known as the Global Carbon Flux Dataset (GCFD) has been released. In this study, we assessed the reliability of the GCFD by comparing it with existing data products, including two machine learning products (FLUXCOM and NIES (National Institute for Environmental Studies)), two ecosystem model products (TRENDY and EC-LUE (eddy covariance–light use efficiency model)), and one remote sensing product (Global Land Surface Satellite), on both site and global scales. Our findings indicate that, in terms of average absolute difference, the spatial distribution of the GCFD is most similar to the NIES product, albeit with slightly larger discrepancies compared to the other two types of products. When using site observations as the benchmark, gross primary production (GPP), respiration of ecosystem (RECO), and net ecosystem exchange of machine learning products exhibit higher R2 (ranging from 0.57 to 0.85, 0.53–0.79, and 0.31–0.70, respectively) compared to model products and remote sensing products. Furthermore, we analyzed the spatial and temporal distribution characteristics of carbon fluxes in various regions. The results demonstrate an upward trend in both GPP and RECO over the past two decades, while NEE exhibits an opposite trend. This trend is particularly pronounced in tropical regions, where higher GPP is observed in tropical, subtropical, and oceanic climate zones. Additionally, two remote sensing variables that influence changes in carbon fluxes, i.e., fraction absorbed photosynthetically active radiation and leaf area index, exhibit relatively consistent spatial and temporal characteristics. Overall, our study can provide valuable insights into different types of carbon flux products and contribute to understanding the general features of global carbon fluxes.","PeriodicalId":37615,"journal":{"name":"Climate","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136210424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The European Green Deal comprises various policy initiatives with the goal of reaching carbon neutrality by 2050. The “Fit for 55 packages” include the Social Climate Fund, which aims to help, among others, vulnerable households and transport users meet the costs of the green energy transition. Thus, analyzing households’ expenditures and the associated carbon emissions is crucial to achieving a net-zero society. In the present study, we combine scenarios of households’ expenditures according to the Classification of Individual Consumption According to Purpose with economic decoupling scenarios to assess, for the first time, the European carbon budget allocation on a consumption basis. Expenditure projections based on socioeconomic scenarios were calculated using the Bayesian structural time series, and the associated emissions were estimated through the greenhouse gas intensity of the Gross Domestic Product. The model can be used to report the carbon budget of households and monitor the effectiveness of the measures funded by the Social Climate Fund. However, the emissions burden obtained by means of averaged greenhouse gas intensity of Gross Domestic Product results in a rough approximation of outcomes, and more accurate indicators should be developed across the member states.
{"title":"Assessing the Emissions Related to European Households’ Expenditures and Their Impact on Achieving Carbon Neutrality","authors":"Ilaria Perissi, Davide Natalini, Aled Jones","doi":"10.3390/cli11100203","DOIUrl":"https://doi.org/10.3390/cli11100203","url":null,"abstract":"The European Green Deal comprises various policy initiatives with the goal of reaching carbon neutrality by 2050. The “Fit for 55 packages” include the Social Climate Fund, which aims to help, among others, vulnerable households and transport users meet the costs of the green energy transition. Thus, analyzing households’ expenditures and the associated carbon emissions is crucial to achieving a net-zero society. In the present study, we combine scenarios of households’ expenditures according to the Classification of Individual Consumption According to Purpose with economic decoupling scenarios to assess, for the first time, the European carbon budget allocation on a consumption basis. Expenditure projections based on socioeconomic scenarios were calculated using the Bayesian structural time series, and the associated emissions were estimated through the greenhouse gas intensity of the Gross Domestic Product. The model can be used to report the carbon budget of households and monitor the effectiveness of the measures funded by the Social Climate Fund. However, the emissions burden obtained by means of averaged greenhouse gas intensity of Gross Domestic Product results in a rough approximation of outcomes, and more accurate indicators should be developed across the member states.","PeriodicalId":37615,"journal":{"name":"Climate","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136293230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elena Grigorieva, Alexandra Livenets, Elena Stelmakh
Since agricultural productivity is weather and climate-related and fundamentally depends on climate stability, climate change poses many diverse challenges to agricultural activities. The objective of this study is to review adaptation strategies and interventions in countries around the world proposed for implementation to reduce the impact of climate change on agricultural development and production at various spatial scales. A literature search was conducted in June–August 2023 using electronic databases Google Scholar and Scientific Electronic Library eLibrary.RU, seeking the key words “climate”, “climate change”, and “agriculture adaptation”. Sixty-five studies were identified and selected for the review. The negative impacts of climate change are expressed in terms of reduced crop yields and crop area, impacts on biotic and abiotic factors, economic losses, increased labor, and equipment costs. Strategies and actions for agricultural adaptation that can be emphasized at local and regional levels are: crop varieties and management, including land use change and innovative breeding techniques; water and soil management, including agronomic practices; farmer training and knowledge transfer; at regional and national levels: financial schemes, insurance, migration, and culture; agricultural and meteorological services; and R&D, including the development of early warning systems. Adaptation strategies depend on the local context, region, or country; limiting the discussion of options and measures to only one type of approach—"top-down” or “bottom-up”—may lead to unsatisfactory solutions for those areas most affected by climate change but with few resources to adapt to it. Biodiversity-based, or “ecologically intensive” agriculture, and climate-smart agriculture are low-impact strategies with strong ecological modernization of agriculture, aiming to sustainably increase agricultural productivity and incomes while addressing the interrelated challenges of climate change and food security. Some adaptation measures taken in response to climate change may not be sufficient and may even increase vulnerability to climate change. Future research should focus on adaptation options to explore the readiness of farmers and society to adopt new adaptation strategies and the constraints they face, as well as the main factors affecting them, in order to detect maladaptation before it occurs.
{"title":"Adaptation of Agriculture to Climate Change: A Scoping Review","authors":"Elena Grigorieva, Alexandra Livenets, Elena Stelmakh","doi":"10.3390/cli11100202","DOIUrl":"https://doi.org/10.3390/cli11100202","url":null,"abstract":"Since agricultural productivity is weather and climate-related and fundamentally depends on climate stability, climate change poses many diverse challenges to agricultural activities. The objective of this study is to review adaptation strategies and interventions in countries around the world proposed for implementation to reduce the impact of climate change on agricultural development and production at various spatial scales. A literature search was conducted in June–August 2023 using electronic databases Google Scholar and Scientific Electronic Library eLibrary.RU, seeking the key words “climate”, “climate change”, and “agriculture adaptation”. Sixty-five studies were identified and selected for the review. The negative impacts of climate change are expressed in terms of reduced crop yields and crop area, impacts on biotic and abiotic factors, economic losses, increased labor, and equipment costs. Strategies and actions for agricultural adaptation that can be emphasized at local and regional levels are: crop varieties and management, including land use change and innovative breeding techniques; water and soil management, including agronomic practices; farmer training and knowledge transfer; at regional and national levels: financial schemes, insurance, migration, and culture; agricultural and meteorological services; and R&D, including the development of early warning systems. Adaptation strategies depend on the local context, region, or country; limiting the discussion of options and measures to only one type of approach—\"top-down” or “bottom-up”—may lead to unsatisfactory solutions for those areas most affected by climate change but with few resources to adapt to it. Biodiversity-based, or “ecologically intensive” agriculture, and climate-smart agriculture are low-impact strategies with strong ecological modernization of agriculture, aiming to sustainably increase agricultural productivity and incomes while addressing the interrelated challenges of climate change and food security. Some adaptation measures taken in response to climate change may not be sufficient and may even increase vulnerability to climate change. Future research should focus on adaptation options to explore the readiness of farmers and society to adopt new adaptation strategies and the constraints they face, as well as the main factors affecting them, in order to detect maladaptation before it occurs.","PeriodicalId":37615,"journal":{"name":"Climate","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135350578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Benedito Cláudio da da Silva, Rebeca Meloni Virgílio, Luiz Augusto Horta Nogueira, Paola do Nascimento Silva, Filipe Otávio Passos, Camila Coelho Welerson
Study region: The Três Marias 396 MW power plant located on the São Francisco River in Brazil. Study focus: Hydropower generation is directly and indirectly affected by climate change. It is also a relevant source of energy for electricity generation in many countries. Thus, methodologies need to be developed to assess the impacts of future climate scenarios. This is essential for effective planning in the energy sector. Energy generation at the Três Marias power plant was estimated using the water balance of the reservoir and the future stream flow projections to the power plant, for three analysis periods: FUT1 (2011–2040); FUT2 (2041–2070); and FUT3 (2071–2100). The MGB-IPH hydrological model was used to assimilate precipitation and other climatic variables from the regional Eta climatic model, via global models HadGEM2-ES and MIROC5 for scenarios RCP4.5 and RCP8.5. New hydrological insights for the region: The results show considerable reductions in stream flows and consequently, energy generation simulations for the hydropower plant were also reduced. The average power variations for the Eta-MIROC5 model were the mildest, around 7% and 20%, while minimum variations for the Eta-HadGEM2-ES model were approximately 35%, and almost 65% in the worst-case scenario. These results reinforce the urgent need to consider climate change in strategic Brazilian energy planning.
{"title":"Assessment of Climate Change Impact on Hydropower Generation: A Case Study for Três Marias Power Plant in Brazil","authors":"Benedito Cláudio da da Silva, Rebeca Meloni Virgílio, Luiz Augusto Horta Nogueira, Paola do Nascimento Silva, Filipe Otávio Passos, Camila Coelho Welerson","doi":"10.3390/cli11100201","DOIUrl":"https://doi.org/10.3390/cli11100201","url":null,"abstract":"Study region: The Três Marias 396 MW power plant located on the São Francisco River in Brazil. Study focus: Hydropower generation is directly and indirectly affected by climate change. It is also a relevant source of energy for electricity generation in many countries. Thus, methodologies need to be developed to assess the impacts of future climate scenarios. This is essential for effective planning in the energy sector. Energy generation at the Três Marias power plant was estimated using the water balance of the reservoir and the future stream flow projections to the power plant, for three analysis periods: FUT1 (2011–2040); FUT2 (2041–2070); and FUT3 (2071–2100). The MGB-IPH hydrological model was used to assimilate precipitation and other climatic variables from the regional Eta climatic model, via global models HadGEM2-ES and MIROC5 for scenarios RCP4.5 and RCP8.5. New hydrological insights for the region: The results show considerable reductions in stream flows and consequently, energy generation simulations for the hydropower plant were also reduced. The average power variations for the Eta-MIROC5 model were the mildest, around 7% and 20%, while minimum variations for the Eta-HadGEM2-ES model were approximately 35%, and almost 65% in the worst-case scenario. These results reinforce the urgent need to consider climate change in strategic Brazilian energy planning.","PeriodicalId":37615,"journal":{"name":"Climate","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135480757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mikhail Varentsov, Mikhail Krinitskiy, Victor Stepanenko
This study considers the problem of approximating the temporal dynamics of the urban-rural temperature difference (ΔT) in Moscow megacity using machine learning (ML) models and predictors characterizing large-scale weather conditions. We compare several ML models, including random forests, gradient boosting, support vectors, and multi-layer perceptrons. These models, trained on a 21-year (2001–2021) dataset, successfully capture the diurnal, synoptic-scale, and seasonal variations of the observed ΔT based on predictors derived from rural weather observations or ERA5 reanalysis. Evaluation scores are further improved when using both sources of predictors simultaneously and involving additional features characterizing their temporal dynamics (tendencies and moving averages). Boosting models and support vectors demonstrate the best quality, with RMSE of 0.7 K and R2 > 0.8 on average over 21 years. For three selected summer and winter months, the best ML models forced only by reanalysis outperform the comprehensive hydrodynamic mesoscale model COSMO, supplied by an urban canopy scheme with detailed city-descriptive parameters and forced by the same reanalysis. However, for a longer period (1977–2023), the ML models are not able to fully reproduce the observed trend of ΔT increase, confirming that this trend is largely (by 60–70%) driven by megacity growth. Feature importance assessment indicates the atmospheric boundary layer height as the most important control factor for the ΔT and highlights the relevance of temperature tendencies as additional predictors.
{"title":"Machine Learning for Simulation of Urban Heat Island Dynamics Based on Large-Scale Meteorological Conditions","authors":"Mikhail Varentsov, Mikhail Krinitskiy, Victor Stepanenko","doi":"10.3390/cli11100200","DOIUrl":"https://doi.org/10.3390/cli11100200","url":null,"abstract":"This study considers the problem of approximating the temporal dynamics of the urban-rural temperature difference (ΔT) in Moscow megacity using machine learning (ML) models and predictors characterizing large-scale weather conditions. We compare several ML models, including random forests, gradient boosting, support vectors, and multi-layer perceptrons. These models, trained on a 21-year (2001–2021) dataset, successfully capture the diurnal, synoptic-scale, and seasonal variations of the observed ΔT based on predictors derived from rural weather observations or ERA5 reanalysis. Evaluation scores are further improved when using both sources of predictors simultaneously and involving additional features characterizing their temporal dynamics (tendencies and moving averages). Boosting models and support vectors demonstrate the best quality, with RMSE of 0.7 K and R2 > 0.8 on average over 21 years. For three selected summer and winter months, the best ML models forced only by reanalysis outperform the comprehensive hydrodynamic mesoscale model COSMO, supplied by an urban canopy scheme with detailed city-descriptive parameters and forced by the same reanalysis. However, for a longer period (1977–2023), the ML models are not able to fully reproduce the observed trend of ΔT increase, confirming that this trend is largely (by 60–70%) driven by megacity growth. Feature importance assessment indicates the atmospheric boundary layer height as the most important control factor for the ΔT and highlights the relevance of temperature tendencies as additional predictors.","PeriodicalId":37615,"journal":{"name":"Climate","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135894946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The diurnal temperature range (DTR) is a significant indicator of climate change, and a previous study has shown its impact on human health. However, research investigating the influence of DTR on road traffic accidents is scarce. Thus, this study aims to evaluate the impact of changes in DTR on road traffic accidents. The present study employs two methods to address the complexities of road accidents. Firstly, panel data from 20 cities and counties in Taiwan are utilized, and the autoregressive distributed lag (ARDL) model is employed for estimation. Secondly, distributed lag non-linear models (DLNMs) are used with quasi-Poisson regression analysis to assess the DTR’s lagged and non-linear relationships with road accidents using time series data from six Taiwanese metropolitan cities. The study results indicate that a decrease of 1 °C in DTR raises long-term road traffic accidents by 17.1%. In the short term, the impact of declining DTR on road accidents is around 4%. Moreover, the effect of low DTR values differs in each city in Taiwan. Three cities had high levels of road accidents, as evidenced by an increase in the relative risk value; two cities had moderate responses; and one city had a relatively lower response compared to high DTR values. Finally, based on the cumulative relative risk estimations, the study found that a low diurnal temperature range is linked to a high road traffic accident rate, especially during the lag-specific 0–5 months. The findings of this study offer fresh evidence of the negative impact of climate factor on road traffic accidents.
{"title":"Dynamic and Non-Linear Analysis of the Impact of Diurnal Temperature Range on Road Traffic Accidents","authors":"Yuo-Hsien Shiau, Su-Fen Yang, Rishan Adha, Giia-Sheun Peng, Syamsiyatul Muzayyanah","doi":"10.3390/cli11100199","DOIUrl":"https://doi.org/10.3390/cli11100199","url":null,"abstract":"The diurnal temperature range (DTR) is a significant indicator of climate change, and a previous study has shown its impact on human health. However, research investigating the influence of DTR on road traffic accidents is scarce. Thus, this study aims to evaluate the impact of changes in DTR on road traffic accidents. The present study employs two methods to address the complexities of road accidents. Firstly, panel data from 20 cities and counties in Taiwan are utilized, and the autoregressive distributed lag (ARDL) model is employed for estimation. Secondly, distributed lag non-linear models (DLNMs) are used with quasi-Poisson regression analysis to assess the DTR’s lagged and non-linear relationships with road accidents using time series data from six Taiwanese metropolitan cities. The study results indicate that a decrease of 1 °C in DTR raises long-term road traffic accidents by 17.1%. In the short term, the impact of declining DTR on road accidents is around 4%. Moreover, the effect of low DTR values differs in each city in Taiwan. Three cities had high levels of road accidents, as evidenced by an increase in the relative risk value; two cities had moderate responses; and one city had a relatively lower response compared to high DTR values. Finally, based on the cumulative relative risk estimations, the study found that a low diurnal temperature range is linked to a high road traffic accident rate, especially during the lag-specific 0–5 months. The findings of this study offer fresh evidence of the negative impact of climate factor on road traffic accidents.","PeriodicalId":37615,"journal":{"name":"Climate","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135828866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrew Ezra, Kai Zhu, Lóránt Dénes Dávid, Barnabas Nuhu Yakubu, Krisztian Ritter
The impact of climate change on river systems is a multifaceted threat to the environment, affecting various aspects of ecosystems. The Upper Benue River Basin (UBRB) in Nigeria is an area of concern, as river flow and water levels are crucial for irrigation and transportation. In this study, we investigate the impact of climate change on the hydrology of the UBRB using data on rainfall, temperature, relative humidity, wind speed, river discharge, and water level. Trend, correlation, and stepwise regression analyses were conducted using Excel and SPSS 20 to analyze the data. The results indicate that the UBRB is experiencing climate change, as evidenced by annual decreases in rainfall and relative humidity and increases in maximum and minimum temperatures. Specifically, mean annual rainfall and relative humidity exhibit a negative trend, while the maximum and minimum temperature exhibit a positive trend. Furthermore, we found that rainfall and relative humidity have a significant positive relationship with river discharge and level (p < 0.01), whereas maximum temperature and wind speed have a significant negative relationship with water discharge and level. We also identified wind speed and rainfall as the critical climatic indices influencing river discharge, accounting for 21.7% of the variation in river discharge within the basin (R2 = 21.7). Based on these findings, we conclude that increases in rainfall and relative humidity will lead to significant increases in river discharge and level, while increases in wind speed and maximum temperature will decrease river discharge and level. Moreover, wind speed and rainfall are the critical climatic indices influencing river discharge, whereas relative humidity, wind speed, and rainfall are the critical climatic indices influencing water level. Thus, we recommend constructing more reservoirs (dams) to mitigate the negative trend in rainfall and encourage climate change control, such as afforestation among the population of the region. These findings have important implications for understanding the impact of climate change on river systems and developing effective strategies to mitigate its effects.
{"title":"Assessing the Hydrological Impacts of Climate Change on the Upper Benue River Basin in Nigeria: Trends, Relationships, and Mitigation Strategies","authors":"Andrew Ezra, Kai Zhu, Lóránt Dénes Dávid, Barnabas Nuhu Yakubu, Krisztian Ritter","doi":"10.3390/cli11100198","DOIUrl":"https://doi.org/10.3390/cli11100198","url":null,"abstract":"The impact of climate change on river systems is a multifaceted threat to the environment, affecting various aspects of ecosystems. The Upper Benue River Basin (UBRB) in Nigeria is an area of concern, as river flow and water levels are crucial for irrigation and transportation. In this study, we investigate the impact of climate change on the hydrology of the UBRB using data on rainfall, temperature, relative humidity, wind speed, river discharge, and water level. Trend, correlation, and stepwise regression analyses were conducted using Excel and SPSS 20 to analyze the data. The results indicate that the UBRB is experiencing climate change, as evidenced by annual decreases in rainfall and relative humidity and increases in maximum and minimum temperatures. Specifically, mean annual rainfall and relative humidity exhibit a negative trend, while the maximum and minimum temperature exhibit a positive trend. Furthermore, we found that rainfall and relative humidity have a significant positive relationship with river discharge and level (p < 0.01), whereas maximum temperature and wind speed have a significant negative relationship with water discharge and level. We also identified wind speed and rainfall as the critical climatic indices influencing river discharge, accounting for 21.7% of the variation in river discharge within the basin (R2 = 21.7). Based on these findings, we conclude that increases in rainfall and relative humidity will lead to significant increases in river discharge and level, while increases in wind speed and maximum temperature will decrease river discharge and level. Moreover, wind speed and rainfall are the critical climatic indices influencing river discharge, whereas relative humidity, wind speed, and rainfall are the critical climatic indices influencing water level. Thus, we recommend constructing more reservoirs (dams) to mitigate the negative trend in rainfall and encourage climate change control, such as afforestation among the population of the region. These findings have important implications for understanding the impact of climate change on river systems and developing effective strategies to mitigate its effects.","PeriodicalId":37615,"journal":{"name":"Climate","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135719685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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