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Climate change is a very debated topic among academia, national and international institutions. Therefore, policies and tools for diminishing pollutant emissions are in place in a number of countries. Among them, taxation and renewable energy use seem to be among the most important. This paper aims at testing the impact of certain tools for climate change policy implementation, such as environmental taxes, renewable energy use, real productivity, employment rate taking into consideration the level of economic development and the GINI coefficient, on the greenhouse gas emissions in two important sectors of the economy: industry and commerce. Panel data analysis is used for a cluster of nine developing countries of the European Union that have the per capita GDP at purchasing power parity lower than 80% of the EU average, during 2008-2021. Similar to other studies, the results show a negative relationship between environmental taxation and greenhouse gas emissions in the industrial sector, and a positive one in the commercial sector. The latter is explained by the fact that transport, which is a main pollutant sector, is also one of the most difficult sectors to achieve green transition, given high associated costs. The analysis also shows that renewable energy use discourages the emissions of greenhouse gases, both in the industrial and commercial sectors, so that fostering investment in renewables is an important factor for addressing climate change and promoting a sustainable growth. [ABSTRACT FROM AUTHOR]

Achieving the net zero emissions target that was set in the Paris Agreement to mitigate the risks of climate change seems increasingly difficult as countries and sectors of the economy are falling behind the expected trajectory. The building and construction industry, as one of the main contributors to global emissions, has an essential role to play toward this aim. Net zero emissions target has been introduced to this sector as well; however, achieving it is a very challenging and complex task. Many studies have been undertaken on implementing different measures and strategies to reduce the industry's carbon footprint. These studies identified many challenges and barriers in transforming the industry. This paper aims to provide a systematic review of challenges that organisations face in delivering new net zero emissions buildings. The relevant journal articles published since the Paris Agreement were identified and analysed using mixed-method data analysis, including quantitative (science mapping) and qualitative (thematic) analysis. The result showed increased attention to the subject over this period, with China, the UK, and Australia being the top contexts for research. The most discussed groups of barriers were "economic", "knowledge", and "technical", respectively, followed by "organisational", "market", "technological", and "legal" barriers. [ABSTRACT FROM AUTHOR]

While radiocarbon (14C) abundance in standing stocks of soil carbon has been used to evaluate rates of soil carbon turnover on timescales of several years to centuries, soil-respired 14CO2 measurements are an important tool for identifying more immediate responses to disturbance and climate change. Soil 14CO2 data are often temporally sparse, however, and could be interpreted better with more context for typical seasonal ranges and trends. We report on a semi-high-frequency sampling campaign to distinguish physical and biological drivers of soil 14CO2 at a temperate forest site in Northern Wisconsin, USA. We sampled 14CO2 profiles every three weeks during snow-free months through 2012, in three intact plots and one trenched plot that excluded roots. Respired 14CO2 declined through the summer in intact plots, shifting from an older C composition that contained more bomb 14C, to a younger composition more closely resembling present 14C levels in the atmosphere. In the trenched plot respired 14C was variable but remained comparatively higher than in intact plots, reflecting older bomb- enriched 14C sources. Although respired 14CO2 from intact plots correlated with soil moisture, related analyses did not support a clear cause-and-effect relationship with moisture. The initial decrease in 14CO2 from spring to midsummer could be explained by increases in 14C-deplete root respiration; however, 14CO2 continued to decline in late summer after root activity decreased. We also investigated whether soil moisture impacted vertical partitioning of CO2 production, but found this had little effect on respired 14CO2 because CO2 contained modern bomb-C at depth, even in the trenched plot. This surprising result contrasted with decades to centuries-old pre-bomb CO2 produced in lab incubations of the same soils. Our results suggest that root-derived C and other recent C sources had dominant impacts on 14CO2 in situ, even at depth. We propose that 14CO2 may have declined through late summer in intact plots because of continued microbial turnover of root-derived C, following declines in root respiration. Our results agree with other studies showing large seasonal fluctuations in respired Δ14CO2, and suggest root C inputs are an important driver. [ABSTRACT FROM AUTHOR]

Hydroacoustic data document the occurrence of 5 flare clusters and several single flares from which bubbles rise through the entire water column from an active seep site at 40 m water depth in the central North Sea. We investigated the difference in dissolved methane distributions along a 6 km transect crossing this seep site during a period of seasonal summer stratification (July 2013) and a period of well mixed winter water column (January 2014). Dissolved methane accumulated below the seasonal thermocline in summer with a median concentration of 390 nM, whereas during winter, methane concentrations were much lower (median concentration of nM) and punctually elevated due to bubble transport. High resolution methane analysis by an underwater mass-spectrometer confirmed our summer results and were used to document prevailing stratification over the tidal cycle. Although sufficient methane was available, microbial methane oxidation was limited during both seasons. Measured and averaged rate constants (k') using Michaelis Menten kinetics were on the order of 0.01 days-1, equivalent to a turnover time of 100 days. Time series measurements indicated an uptake of only 5-6% of the gas after 4 days, and no known methanotrophs and pmoA-genes were detected. Estimated methane fluxes indicate that horizontal eddy transport rapidly disperses dissolved methane, vertical transport becomes dominant during phases of high wind speeds, and relative to these processes, microbial methane oxidation appears to be comparably low. To bridge the discrete field data we developed a 1-D seasonal model using available yearlong records of wind speed, surface temperature and thermocline depth. The model simulations show a peak release of methane at the beginning of fall when the water column becomes mixed. Consistent with our field data, inclusion of microbial methane oxidation does not change the model results significantly, thus microbial oxidation appears to be not sufficient to notably reduce methane during summer stratification before the peak release in fall. [ABSTRACT FROM AUTHOR]

Conducting a vulnerability and risk assessment (VRA) is a critical step in adaptation planning and implementation. This research paper has assessed how the co-developed approach helped the Government of Nepal and stakeholders in Nepal to agree on and operationalize the vulnerability and risk assessment framework and what works best for the country's context. The methodological framework consists of eight steps including (i) scoping, (ii) reviewing the framework (iii) identifying data sources (iv) exploring data sources, nature, and character (v) data collection, tabulation, filtration, and normalization (vi) weightage and composite value (vii) analysis of the data (viii) identifying climate change impact, vulnerability, and risk. The output of the vulnerability and risk assessment in Nepal presents a compilation of impacts, vulnerabilities, and risks and a description of their context, root causes, and trends. These research-based assessment can be used to draw some possible adaptation options and improved decision-making at the national and sub-national levels. However, there were some challenges faced in analyzing the vulnerability and risks based on the indicators. There was a lack of a multi-year, complete, and uniform database, and difficulties in developing scenarios of hazards due to unclarity on climate change attribution. The lessons from this paper will be important for designing a more practical and country-driven VRA framework and methodology for other countries. [ABSTRACT FROM AUTHOR]

Citizen science is a research practice that relies on public contributions of data. The strong recognition of its educational value combined with the need for novel methods to handle subsequent large and complex data sets raises the question: Is citizen science effective at science? A quantitative assessment of the contributions of citizen science for its core purpose – scientific research – is lacking. We examined the contribution of citizen science to a review paper by ornithologists in which they formulated ten central claims about the impact of climate change on avian migration. Citizen science was never explicitly mentioned in the review article. For each of the claims, these ornithologists scored their opinions about the amount of research effort invested in each claim and how strongly the claim was supported by evidence. This allowed us to also determine whether their trust in claims was, unwittingly or not, related to the degree to which the claims relied primarily on data generated by citizen scientists. We found that papers based on citizen science constituted between 24 and 77% of the references backing each claim, with no evidence of a mistrust of claims that relied heavily on citizen-science data. We reveal that many of these papers may not easily be recognized as drawing upon volunteer contributions, as the search terms “citizen science” and “volunteer” would have overlooked the majority of the studies that back the ten claims about birds and climate change. Our results suggest that the significance of citizen science to global research, an endeavor that is reliant on long-term information at large spatial scales, might be far greater than is readily perceived. To better understand and track the contributions of citizen science in the future, we urge researchers to use the keyword “citizen science” in papers that draw on efforts of non-professionals. [ABSTRACT FROM AUTHOR]

Land surface albedo is a relevant variable in many climatic, environmental, and hydrological studies; its monitoring allows researchers to identify changes on the Earth’s surface. The open satellite data that is provided by the USGS/NASA Landsat mission is quite suitable for estimating this parameter through the remote sensing technique. The purpose of this paper is to evaluate the potentialities of the new Landsat 9 data for retrieving Earth’s albedo by applying da Silva et al.’s algorithm (developed in 2016 for the Landsat 8 data) using the Google Earth Engine cloud platform and R software. Two urban areas in Southern Italy with similar geomorphologic and climatic characteristics were chosen as study sites. After obtaining thematic maps of the albedos here, a statistical analysis and comparison among the Landsat 8 and Landsat 9 results was performed considering the entire study areas and each land use/land cover class that is provided by the Copernicus Urban Atlas 2018. This approach was also applied to the data after being filtered through Tukey’s test (used to detect and remove outliers). The analysis showed a very good correlation between the Landsat 8 and Landsat 9 estimations (ρ > 0.94 for both sites), with some exceptions that were related to some mis-corresponding values. Furthermore, the Landsat 8 and Landsat 9 outliers were generally overlapping. In conclusion, da Silva et al.’s approach appears to also be reasonably applicable to the Landsat 9 data despite some radiometric differences. [ABSTRACT FROM AUTHOR]

In order to effectively model the potential impacts of future climate change, there is a requirement for climate data inputs which (a) are of high spatial and temporal resolution, (b) explore a range of future climate change scenarios, (c) are consistent with historical observations in the historical period, and (d) provide an exploration of climate model uncertainty. This paper presents a suite of climate projections for the United Kingdom that conform to these requirements: CHESS-SCAPE. CHESS-SCAPE is a 1 km resolution dataset containing 11 near-surface meteorological variables that can be used to as input to many different impact models. The variables are available at several time resolutions, from daily to decadal means, for the years 1980–2080. It was derived from the state-of-the art regional climate projections in the United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km ensemble, downscaled to 1 km using a combination of physical and empirical methods to account for local topographic effects. CHESS-SCAPE has four ensemble members, which were chosen to span the range of temperature and precipitation change in the UKCP18 ensemble, representing the ensemble climate model uncertainty. CHESS-SCAPE consists of projections for four emissions scenarios, given by the Representative Concentration Pathways 2.6, 4.5, 6.0 and 8.5, which were derived from the UKCP18 RCM RCP8.5 scenarios using time shifting and pattern scaling. These correspond to UK annual warming projections of between 0.9–1.9 K for RCP2.6 up to 2.8–4.3 K for RCP8.5 between 1980–2000 and 2060–2080. Little change in annual precipitation is projected, but larger changes in seasonal precipitation are seen with some scenarios projecting large increases in precipitation in the winter (up to 22 %) and large decreases in the summer (up to - 39 %). All four RCP scenarios and ensemble members are also provided with bias correction, using the CHESS-met historical gridded dataset as a baseline. With high spatial and temporal resolution, an extensive range of warming scenarios and multiple ensemble members, CHESS-SCAPE provides a comprehensive data resource for modellers of climate change impacts in the UK. The CHESS-SCAPE data are available for download from the NERC EDS Centre for Environmental Data Analysis: 10.5285/8194b416cbee482b89e0dfbe17c5786c. [ABSTRACT FROM AUTHOR]

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management sustainability. However, the wider adoption of AI in water resource management faces challenges, such as data heterogeneity, stakeholder education, and high costs. To provide an overview of ML applications in water resource management, this research focuses on core fundamentals, major applications (prediction, clustering, and reinforcement learning), and ongoing issues to offer new insights. More specifically, after the in-depth illustration of the ML algorithmic taxonomy, we provide a comparative mapping of all ML methodologies to specific water management tasks. At the same time, we include a tabulation of such research works along with some concrete, yet compact, descriptions of their objectives at hand. By leveraging ML tools, we can develop sustainable water resource management plans and address the world's water supply concerns effectively. [ABSTRACT FROM AUTHOR]

According to the climate change scenario, climate change in the Korean Peninsula is expected to worsen due to extreme temperatures, with effects such as rising average temperatures, heat waves, and droughts. In Republic of Korea, which relies on foreign countries for the supply of forage crops, a decrease in the productivity of forage crops is expected to cause increased damage to the domestic livestock industry. In this paper, to solve the issue of climate vulnerability for forage crops, we performed a study to predict the productivity of forage crops in relation to climate change. We surveyed and compiled not only forage crop production data from various regions, but also experimental cultivation production data over several years from reports of the Korea Institute of Animal Science and Technology. Then, we crawled related climate data from the Korea Meteorological Administration. Therefore, we were able to construct a basic database for forage crop production data and related climate data. Using the database, a production prediction model was implemented, applying a multivariate regression analysis and deep learning regression. The key factors were determined as a result of analyzing the changes in forage crop production due to climate change. Using the prediction model, it could be possible to forecast the shifting locations of suitable cultivation areas. As a result of our study, we were able to construct electromagnetic climate maps for forage crops in Republic of Korea. It can be used to present region-specific agricultural insights and guidelines for cultivation technology for forage crops against climate change. [ABSTRACT FROM AUTHOR]

Numerical models have been developed to investigate and understand responses of biogeochemical cycle to global changes. Steady state, when a system is in dynamic equilibrium, is generally required to initialize these model simulations. However, the spin‐up process that is used to achieve steady state pose a great burden to computational resources, limiting the efficiency of global modeling analysis on biogeochemical cycles. This study introduces a new Semi‐Analytical Spin‐Up (SASU) to tackle this grand challenge. We applied SASU to Community Land Model version 5 and examined its computational efficiency and accuracy. At the Brazil site, SASU is computationally 7 times more efficient than (or saved up to 86% computational cost in comparison with) the traditional native dynamics (ND) spin‐up to reach the same steady state. Globally, SASU is computationally 8 times more efficient than the accelerated decomposition spin‐up and 50 times more efficient than ND. In summary, SASU achieves the highest computational efficiency for spin‐up on site and globally in comparison with other spin‐up methods. It is generalizable to wide biogeochemical models and thus makes computationally costly studies (e.g., parameter perturbation ensemble analysis and data assimilation) possible for a better understanding of biogeochemical cycle under climate change. Plain Language Summary: Land carbon cycle models, a critical component of earth system models, have been developed to investigate and understand ecological responses to global changes. However, the model spin‐up process to achieve steady state are usually time‐consuming and pose great computational burden. The spin‐up problem hinders our ability to study some key issues in land carbon cycle modeling, such as sensitivity analysis. This paper applies a new SASU framework to accelerate spin‐up of Community Land Model matrix version 5. Compared with traditional native dynamic approach, SASU speeds up the spin‐up by approximately 50 times, achieving the fastest spin‐up speed than all the previously recorded methods. Our SASU method is applicable to biogeochemical models and, therefore, liberates the models for computationally costly studies, such as parameter perturbation ensemble analysis and data assimilation. Key Points: A new semi‐analytical spin‐up (SASU) framework combines the default accelerated spin‐up method and matrix analytical algorithmSASU accelerates CLIM5 spin‐up by tens of times, becoming the fastest method to our knowledgeSASU is applicable to most biogeochemical models and enables computationally costly study, for example, sensitivity analysis [ABSTRACT FROM AUTHOR]

Permaculture is a growing but little researched phenomenon emphasising care for the environment, equity, fair treatment of people and working with—and not against—nature. It thus represents a potential alternative to business as usual, capable of addressing fundamental challenges posed by human‐made climate change. The paper examines a previously ignored site of entrepreneurship by taking a practice perspective, exploring connections between the practice and growth of permaculture and institutional entrepreneurship. It assesses practice‐related and institutional factors affecting the start‐up and operation of permaculture enterprises in the United Kingdom. The study maps and surveys UK Permaculture Association members who have started up their own business and reports on qualitative data from personal interviews with twenty of them. Data analysis employs NVivo software and involves thematic analysis pertaining to the practice, institutional biographies and institutional portfolios of permaculture entrepreneurs. The findings show the importance of permaculture activists' institutional biographies and institutional portfolios to the start‐up and operation of permaculture enterprises and for shaping permaculture‐related practice. The contribution of the paper lies in how it balances attention to individual agency with subfield‐specific, organisational field and macrosocial factors in understanding 'beyond profit' entrepreneurship. [ABSTRACT FROM AUTHOR]

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The spatio-temporal variogram is an important factor in spatio-temporal prediction through kriging, especially in fields such as environmental sustainability or climate change, where spatio-temporal data analysis is based on this concept. However, the traditional spatio-temporal variogram estimator, which is commonly employed for these purposes, is extremely sensitive to outliers. We approach this problem in two ways in the paper. First, new robust spatio-temporal variogram estimators are introduced, which are defined as M-estimators of an original data transformation. Second, we compare the classical estimate against a robust one, identifying spatio-temporal outliers in this way. To accomplish this, we use a multivariate scale-contaminated normal model to produce reliable approximations for the sample distribution of these new estimators. In addition, we define and study a new class of M-estimators in this paper, including real-world applications, in order to determine whether there are any significant differences in the spatio-temporal variogram between two temporal lags and, if so, whether we can reduce the number of lags considered in the spatio-temporal analysis. [ABSTRACT FROM AUTHOR]

As the climate changes, it is important to understand the effects on the environment. Changes in wildland fire risk are an important example. A stochastic lattice-based wildland fire spread model was proposed by Boychuk et al. (2007), followed by a more realistic variant (Braun andWoolford, 2013). Fitting such a model to data from remotely sensed images could be used to provide accurate fire spread risk maps, but an intermediate step on the path to that goal is to verify the model on data collected under experimentally controlled conditions. This paper presents the analysis of data from small-scale experimental fires that were digitally video-recorded. Data extraction and processing methods and issues are discussed, along with an estimation methodology that uses differential equations for the moments of certain statistics that can be derived from a sequential set of photographs from a fire. The interaction between model variability and raster resolution is discussed and an argument for partial validation of the model is provided. Visual diagnostics show that the model is doing well at capturing the distribution of key statistics recorded during observed fires. [ABSTRACT FROM AUTHOR]

Drought periods can have important impacts on plant productivity and ecosystem functioning, but climatic conditions other than the lack of precipitation during droughts have never been quantified and have therefore not been considered explicitly in both experimental and modeling studies. Here, we identify which climatic characteristics deviate from normal during droughts and how these deviations could affect plant responses. Analysis of 609 years of daily data from nine Western European meteorological datasets reveals that droughts in the studied region are consistently associated with more sunshine (+45%), increased mean (+1.6 °C) and maximum (+2.8 °C) air temperatures and vapour pressure deficits that were 51% higher than under normal conditions. These deviations from normal increase significantly as droughts progress. Using the process-model ORCHIDEE, we simulated droughts consistent with the results of the dataset analysis and compared water and carbon exchange of three different vegetation types during such natural droughts and droughts in which only the precipitation was affected. The comparison revealed contrasting responses: carbon loss was higher under natural drought in grasslands, while increased carbon uptake was found especially in decidious forests. This difference was attributed to better access to water reserves in forest ecosystems which prevented drought stress. This demonstrates that the warmer and sunnier conditions naturally associated with droughts can either improve growth or aggravate drought-related stress, depending on water reserves. As the impacts of including or excluding climatic parameters that correlate with drought are substantial, we propose that both experimental and modeling efforts should take into account other environmental factors than merely precipitation. [ABSTRACT FROM AUTHOR]

The agricultural sector and how it relates to climate change is today emerging as a central subject of debate and critique, because it is heavily impacted by, and at the same time, a primary contributor to, climate change. The intertwined, complex relationship between the sector and climate change is among the unprecedented challenges now facing the European Union (EU). The complexity of the relationship calls for the establishment of a sustainable, future climate-proof, adapted and resilient sector with strong adaptive capacity. This paper argues that over the past decades, strong emphasis has been placed on how to mitigate the negative effects of climate change across the sector, causing it to fall behind in terms of adaptation. Although adaptation is now part of the sector's development agenda, sectoral adaptation performance across member states remains low. In order to justify an accelerated adaptation process across the sector, the paper develops a Relative Climate Change Adaption Index (RCCAI) for the sector based on Eurostat data. The analysis shows that there is no single member state across the EU whose agricultural sector can be considered as fully climate-adapted (resilient), and thus validates the hypothesis that adaption efforts must be stepped up across the sector. To ensure continued sectoral adaptive capacity improvement, the paper recommends coherent integration and accelerated implementation of adaptation practices and policies alongside the Common Agricultural Policy (CAP) for the sake of both private and public interests. [ABSTRACT FROM AUTHOR]

The topics of climate change and pollutant emission reduction are dominating societal discussions in many areas. In automotive development, with the introduction of real driving emissions (RDE) testing and the upcoming EU7 legislation, there are endless boundary conditions and potential scenarios that need to be evaluated. In terms of vehicle calibration, this is leading to a strong focus on alternative approaches such as virtual calibration. Due to the flexibility of virtual test environments and the variety of RDE scenarios, the amount of data collected is rapidly increasing. Supporting the calibration engineers in using the available data and identifying relevant information and test scenarios requires efficient approaches to data analysis. This paper therefore discusses the potential of data clustering to support this process. Using a previously developed approach for event detection in emission calibration, a methodology for the automatic categorization of events is presented. Approaches to clustering algorithms (hierarchical, partitioning, and density-based) are discussed and applied to data of interest. Their suitability for different signals is investigated exemplarily, and the relevant inputs are analyzed for their usability in calibration procedures. It is shown which clustering approaches have the potential to be implemented in the vehicle calibration process to provide added value to data evaluation by calibration engineers. [ABSTRACT FROM AUTHOR]

The carbon embodied in buildings is an important proportion of our emissions and needs to be radically reduced in order to support climate change mitigation. The highest proportion of embodied carbon is usually emitted during the product stage, and within the structural elements. Therefore, reducing the carbon embodied in the structural materials is likely to have a major impact. In most buildings, the majority of embodied carbon comes from steel and concrete. But although there are now hundreds of registered Environmental Product Declarations (EPD) for cements and concretes, there has been very limited independent published information comparing the embodied carbon of different concrete mixes and raw materials. This lack of comparative data limits the potential to make appropriate decisions at early design stages leading to low carbon buildings. The authors have recently conducted a review of verified EPD for concrete mixes and for concrete’s key constituents, including cement, identifying the range of carbon coefficients. This paper provides guidance on making use of the coefficient ranges provided in that research: to support the verification of EPD for concrete and its raw materials; in material selection; in assessing building level embodied carbon; in benchmarking; and in the setting of reduction targets. [ABSTRACT FROM AUTHOR]

This paper explores the determinants of carbon taxation by focusing on recent efforts to tax carbon in Washington State. The paper identifies citizen demand, energy interests, and tax structure as possible factors in the emergence of carbon taxation. Washington has relatively high demand for climate action, and is unique with respect to its high reliance on indirect taxation and its low level of local fossil fuel interests. I argue this context, especially the high reliance on indirect taxation, makes taxation more appealing than other carbon pricing alternatives. I then test whether local tax structure shapes preferences for carbon taxation using county level opinion data. I find evidence that support for carbon taxation is higher in states with higher gasoline taxes, but no effect from sales and sin taxes. I conclude summarizing various accounts of each initiative's failure, and highlight a few lessons from the efforts to tax carbon in Washington state. [ABSTRACT FROM AUTHOR]

We compiled and analyzed long-term (1961-2005) zooplankton community data in response to environmental variations in Lake Biwa. Environmental data indicate that Lake Biwa had experienced eutrophication (according to total phosphorus concentration) in the late 1960s and recovered to a normal trophic status around 1985, and then exhibited warming since 1990. Total zooplankton abundance showed a significant correlation with total phytoplankton biomass. Following a classic pattern, cladoceran/calanoid and cyclopoid/calanoid abundance ratio was related positively to eutrophication. Zooplankton community exhibited a significant response to the boom and bust of phytoplankton biomass as a consequence of eutrophication-reoligotriphication and warming. Moreover, our analyses suggest that the Lake Biwa ecosystem exhibited a hierarchical response across trophic levels; that is, higher trophic levels may show a more delayed response or no response to eutrophication than lower ones. We tested the hypothesis that phytoplankton community can better explain the variation of zooplankton community than bulk environmental variables, considering that phytoplankton community may directly affects zooplankton succession through predatorprey interactions. Using a variance partition approach, however, we did not find strong evidence to support this hypothesis. We further aggregate zooplankton according to their feeding types (herbivorous, carnivorous, omnivorous, and parasitic) and taxonomic groups, and analyzed the aggregated data. While the pattern remains similar, the results are less clear comparing with the results based on finely resolved data. Our research explored the efficacy of using zooplankton as bio-indicators to environmental changes at various data resolutions. [ABSTRACT FROM AUTHOR]

Nonstationary is one of the prominent phenomena in the current hydrological time series due to climate change and urban expansion. In this study, using the long time series rainfall data from rain gauges and satellite rainfall data, the trend and abrupt change of rainfall in the Taihu Lake basin, China, are examined by the Mann–Kendall (MK) test and the Pettitt test, using rain gague data. For seven water conservancy zones in this basin, the intensity–duration–frequency curves (IDFs) are obtained using satellite rainfall and the stochastic storm transposition (SST) method, providing a method for rainfall frequency analysis based on nonstationary assumption. The IDFs results between the conventional frequency analysis method with the stationary assumption and the SST-based method are compared. The results show an overall increasing trend of annual total rainfall in the Taihu Lake Basin, with significant changes at most stations. The SST-based results show a significant difference of IDFs in seven conservancy zones, which are linked to nonstationary changes in rainfall series. Our results provide an important reference for understanding the nonstationary changes and nonstationary frequency analysis of extreme rainfall in the Taihu Lake basin. [ABSTRACT FROM AUTHOR]

Physical-natural, economic, social, and spatial factors constitute processes that, when associated, form and give meaning to the spatial complexity of the Metropolitan Region of Rio de Janeiro, configuring it as a susceptible and vulnerable space to the occurrence of climate exceptionalities. Thus, this paper aims to analyze the spatial and temporal variability of extreme precipitation events in the Metropolitan Region of Rio de Janeiro. Daily data regarding 48 rainfall stations were used: 40 stations in a series from 2008 to 2019 and 8 stations concerning the period between 1970 and 2019. The percentile technique was used to establish standard years (1, 10, 90, and 99 percentile) and to define the extremes (99th percentile). The R software was used to perform autocorrelation tests, to extract the RX1Day and RX5Day indices, and to perform the Mann-Kendall, Pettitt, and Sen's Slope tests. The influence of altitude, slope orientation, and sea level was verified on the spatial distribution of precipitation and extreme events. The years with the highest occurrence of extremes, as well as concentrated and persistent events of greater magnitude, are identified as rainy and extremely rainy, namely 1988 e 2009. 90% of extremes occur in the rainy season, especially during December, January, and March. However, April manifests extremes of a greater magnitude. Four stations with negative and two with positive trends were identified. [ABSTRACT FROM AUTHOR]

This study was driven by a need to clarify how variations in climate might affect intense rainfall and the potential for flooding. Sub-daily durations are of particular interest for urban applications. Worldwide, few such observation-based studies exist, which is mainly due to limitations in data. While there are still large discrepancies between precipitation data sets from observations and models, both show that there is a tendency for moist regions to become wetter and for dry regions to become drier. However, changes in extreme conditions may show the opposite sign to those in average conditions. Where changes in observed intense precipitation have been studied, this has typically been for daily durations or longer. The purpose of this two-part study is to examine daily and sub-daily rainfall extremes for evidence of non-stationarity. Here the problem was addressed by supplementing one long record (Part 1) by a set of shorter records for a 30-yr concurrent period (Part 2). Variations in frequency and magnitude of rainfall extremes across durations from 6 min to 72 h were assessed using data from sites in the south-east of Australia. For the analyses presented in this paper, a peaks-over-threshold approach was chosen since it allows investigating changes in frequency as well as magnitude. Non-parametric approaches were used to assess changes in frequency, magnitude, and quantile estimates as well as the statistical significance of changes for one station (Sydney Observatory Hill) for the period 1921 to 2005. Deviations from the long-term average vary with season, duration, and threshold. The effects of climate variations are most readily detected for the highest thresholds. Deviations from the long-term average tend to be larger for frequencies than for magnitudes, and changes in frequency and magnitude may have opposite signs. Investigations presented in this paper show that variations in frequency and magnitude of events at daily durations are a poor indicator of changes at sub-daily durations. Studies like the one presented here should be undertaken for other regions to allow the identification of regions with significant increase/decrease in intense rainfall, whether there are common features with regards to duration and season exhibiting most significant changes (which in turn could lead to establishing a theoretical framework), and assist in validation of projections of rainfall extremes. [ABSTRACT FROM AUTHOR]

Sea surface height (SSH) variability is presented over the Black Sea during 1993–2005. The 1/4° × 1/4° resolution daily SSH fields are formed using optimal interpolation of available altimeter data. SSH variability reveals distinct maxima in the eastern and western basins, reflecting variations in the corresponding gyres. A joint examination of SSH and sea surface temperature (SST) indicates strong relationship between the two only in winter, with correlations as high as 0.6 or more. This would reflect a steric change in sea surface height due to thermal expansion averaged over a relatively deep winter mixed layer. Newly developed SSH fields also demonstrate a switch to the positive mode of SSH starting from the end of 1996 lasting ≈4 yr. Such a climatic shift is found to be strongly related to large-scale teleconnection patterns. Finally, the daily SSH and SST anomaly fields presented in this paper can supplement various applications in the Black Sea, such as examination of biological production and mesoscale eddy dynamics. [ABSTRACT FROM AUTHOR]

Over the past 50 years, temperatures on the Qinghai-Tibet Plateau (QTP) have risen roughly twice as fast as the global average, making it the most unpredictable region of environmental change due to global warming. In this paper, an Environmental Area Index model was developed using data from the Coupled Model Intercomparison Project to assess the ecological risk faced by QTP ecosystems under the influence of climate factors. The results show that ecological risk gradually decreases from northwest to the southeast, and there are different trends in ecological risk for each class in areas with different elevation gradients. As elevation increased, the proportion of potential risk areas gradually decreased, and the proportion of high- and higher-risk areas gradually increased. We predict that in the period 2021–2100, the overall ecological risk change trend on the QTP will not be obvious, but there will be a more obvious change on the vertical gradient. In general, under the existing global climate change scenario, the ecological risk faced by the QTP show a decreasing trend under the influence of climate factors, and the decrease in ecological risks is much higher at higher elevations than at lower elevations. [ABSTRACT FROM AUTHOR]

The basic idea of vibration-based damage identification approaches is that damage causes change in vibration response of structure. So monitoring the vibration response characteristics can be helpful in damage detection. The main limitation in such methods is that these characteristics are also affected by the Environmental and Operational Variability (EOV) that can be incorrectly known as structural damage or sometimes cover actual damages. This paper aims to propose an innovative approach to detect and locate damage considering the EOV conditions. In this regard, an Independent Component Analysis (ICA) based Blind Source Separation (BSS) approach is employed to remove the EOV influences from the time history response of the structure. The beneficial of using the ICA-based BSS method is that there is no need to measure the environmental/operational conditions. Moreover, it is able to remove EOV influences using a limited group of response data monitored during different environmental and operational conditions. Time series analysis is then performed to extract damage-sensitive features. Finally, a statistical tool is employed to damage identification and localization by using EOV independent features. Two recognized benchmark structures are employed for verifying the accuracy of the proposed approach. Results indicate that the proposed method is a time-saving tool and efficiently successful in damage assessment of structures under EOV. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to assess and analyse the dynamics of flash flooding events in Slovenia. The paper examines in particular the frequency of flash floods and their seasonal distribution. The methodology is based on the analysis of historical records and modern flood data. The results of a long-term frequency analysis of 138 flash floods that occurred between 1550 and 2015 are presented. Because of the lack of adequate historical flood data prior to 1950 the main analysis is based on data for the periodbetween1951 and2015, while the analysis of data for the period between1550 and1950 is added as a supplement to the main analysis. Analysis of data for the period after 1950 shows that on average 1.3 flash floods occur each year in Slovenia. The linear trend for the number of flash floods is increasing but is not statistically significant. Despite the fact that the majority of Slovenian rivers have one of the peaks in spring and one of the lows in summer, 90% of flash floods actually occur during meteorological summer or autumn - i.e. between June and November, which shows that discharge regimes and flood regimes are not necessarily related. Because of the lack of flood records from the more distant past as well as the large variability of flash flood events in the last several decades, we cannot provide a definitive answer to the question about possible changes in their frequency and seasonality by relying solely on the detected trends. Nevertheless, considering the results of analysis and future climate change scenarios the frequency of flash floods in Slovenia could increase while the period of flash flood occurrence could be extended. [ABSTRACT FROM AUTHOR]

Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast. For global monthly mean temperature series, considering the strong potential of deep neural network for extracting data features, this paper proposes a data-driven model, ResGraphNet, which improves the prediction accuracy of time series by an embedded residual module in GraphSAGE layers. The experimental results of a global mean temperature dataset, HadCRUT5, show that compared with 11 traditional prediction technologies, the proposed ResGraphNet obtains the best accuracy. The error indicator predicted by the proposed ResGraphNet is smaller than that of the other 11 prediction models. Furthermore, the performance on seven temperature data sets shows the excellent generalization of the ResGraphNet. Finally, based on our proposed ResGraphNet, the predicted 2022 annual anomaly of global temperature is 0.74722 °C, which provides confidence for limiting warming to 1.5 °C above pre-industrial levels. [ABSTRACT FROM AUTHOR]

Climate change, the global energy footprint, and strategies for sustainable development have become topics of considerable political and public interest. The public debate is informed by an exponentially growing amount of data and there are diverse partisan interest when it comes to interpretation. We therefore believe that data analysis methods are called for that provide results which are intuitively understandable even to non-experts. Moreover, such methods should be efficient so that non-experts users can perform their own analysis at low expense in order to understand the effects of different parameters and influential factors. In this paper, we discuss a new technique for factorizing data matrices that meets both these requirements. The basic idea is to represent a set of data by means of convex combinations of extreme data points. This often accommodates human cognition. In contrast to established factorization methods, the approach presented in this paper can also determine over-complete bases. At the same time, convex combinations allow for highly efficient matrix factorization. Based on techniques adopted from the field of distance geometry, we derive a linear time algorithm to determine suitable basis vectors for factorization. By means of the example of several environmental and developmental data sets we discuss the performance and characteristics of the proposed approach and validate that significant efficiency gains are obtainable without performance decreases compared to existing convexity constrained approaches. [ABSTRACT FROM AUTHOR]

To understand complex networks like online social networks (OSNs), one must explore them. This gives a partial view of the real object, which is generally assumed to be representative of the whole, like an iceberg. OSNs are growing rapidly, not only in the number of users they attract, but also in the mass of data produced in a short period of time. Once created, the data attracts the attention of other users, who interact with it. The purpose of this study is to evaluate the impact of keywords used on content attraction, through an analysis of the attraction's different modes as well as the nature of the interactions of the different communities. The study concerns a crucial topic: the environment and climate change. This paper looks at the keywords used on Twitter and their impact on the interactions of different users for this complex network. [ABSTRACT FROM AUTHOR]

Natural conditions other than the ecological conditions of the Chinese gene center (as 34-38° latitude and 600 to 2400 m above sea level), mainly dry subtropical, i.e. Mediterranean effects, facilitated the development of new forms and varieties (Scorza & Okie, 1991; Faust & Timon, 1995). Probably the primary cause of nectarines, this could also be the primary cause of mutations (probably about 2000 years ago) (Roach, 1985; Surányi, 1985). During the long domestication of peaches, its natural occurrence increased, which was greatly enhanced by its ecological and mutational ability and the organoleptical values of its fruit (Hedrick, 1917; Roach, 1985; Scorza & Okie, 1991; Faust et al., 2011). Through the Ellenberg-Borhidi model and its refinement, the author has demonstrated the suitability of peaches in a broad climate zone based on the relative ecological and biological values of 700 varieties. Among the varieties, clone cultivars and hybrids were Hungarian selected and crossed form, because the diverse environmental conditions of the Carpathian Basin and the past and present size of cultivation were representative (Faust & Timon, 1995; Timon, 2000). It can be concluded from the present relative ecological data that the average standard deviation is below 12% for both peach and nectarine varieties, but the relative biological values were very different. Comparison of cultivars or classical (downy) peaches (n = 562) and nectarines (n = 138) in terms of environmental values confirmed the difference in heat demand and salt tolerance of the two groups of varieties. The pictures of the paper also demonstrated the rich diversity of this fruit species, and after analyzing the apricot and plum varieties (Surányi 2014, 2018), the peculiarities of the relative ecological and biological values of peaches were confirmed. [ABSTRACT FROM AUTHOR]

The paper presents the results of time-related changes in maximum temperatures in lakes. The analysis was carried out on the basis of 9 lakes located in the northern part of Poland. The analysis was based on daily water and air temperatures in the period 1971–2015. Mann–Kendall's and Sen's tests were applied to determine the directions and rates of change of maximum air and water temperatures. The average increase of maximum water temperature in analysed lakes was found to be 0.39 °C dec–1, while the warming trend of the maximum air temperature was 0.48 °C dec–1. Cluster analysis (CA) was used to group lakes characterised by similar changes of maximum water temperature. The first group included five lakes in which the values of the maximum temperature trends were 0.41 °C dec–1. In the second cluster the average value of maximum water temperature increase was smaller than in the first cluster (0.36 °C dec–1). Comparing the results of cluster analysis with morphometric data show that in the first cluster lakes are having a greater average depth, maximum depth and water transparency in comparison to the lakes of the second cluster. The study showed significant changes in the maximum annual water temperatures in post-glacial lakes located in Poland in the years 1971-2015. The highest rate of increase in monthly maximum water temperature was recorded in April and the lowest in January and February. The value of maximum water temperature trends was lower than annual air temperature trends. [ABSTRACT FROM AUTHOR]

First-year ice has replaced multiyear ice in the Northern Sea Route area since 2008. In this area, sea ice survival during summer substantially depends on first-year ice thickness at melt onset, and thus monitoring of first-year ice thickness in the freezing period is a key to forecasting sea ice distributions in the following summer. In this paper we introduce a new algorithm to estimate flat first-year ice draft using brightness temperature data measured by the Advanced Microwave Scanning Radiometer-2 (AMSR2). The algorithm uses a gradient ratio (GR) of 18- and 36-GHz vertically polarized brightness temperatures based on decreases in sea ice emissivity in higher AMSR2 frequency channels with thermodynamic growth associated with an increase in volume scattering. Such spectral characteristics of the emissivity are examined by comparing GR values with flat first-year ice draft extracted by mode values of in situ draft data measured by a moored ice profiling sonar. The accuracy of the daily draft estimated from GR values after applying proper noise filters is about 10 cm for a draft range of 0.4–1.2 m. [ABSTRACT FROM AUTHOR]

This paper describes the repackaging and consistent distribution of the world's major atmospheric and oceanic reanalyses. It also presents examples of the usefulness of examining multiple reanalyses. This service will make it much easier for anybody using reanalysis to access multiple datasets using an approach similar to that of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Experienced users as well as students will find the standardized formatted data convenient to use. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to provide a method for perturbing Weather Generators (WGs) for future decades and to assess its effectiveness. Here the procedure is applied to the WG implemented within the UKCP09 package and assessed using data from a Regional Climate Model (RCM) simulation which provides a significant "climate change" between a control run period and a distant future. The WG is normally calibrated on observed data. For this study, data from an RCM control period (1961-1990) was used, then perturbed using the procedure. Because only monthly differences between the RCM control and scenario periods are used to perturb the WG, the direct daily RCM scenario may be considered as unseen data to assess how well the perturbation procedure reproduces the direct RCM simulations for the future. [ABSTRACT FROM AUTHOR]

This paper conducts the assimilating experiments and simulating experiments on typhoon "Aere" (No. 0418), by use of bogus data assimilation (BDA) method combined with advanced microwave sounding unit-A (AMSU-A) data assimilation method in the fifth-generation National Center for Atmospheric Research (NCAR)/Penn State Mesoscale Model Version-3 (MM5V3), the Radiative Transfer for TIROS-N Operational Vertical Sounder Version-7 (RTTOV) model, and their adjoint models. The Bogus data constructed with BDA technique are mainly located at sea level, while the peak energy contribution levels of the sounder channels selected in AMSU-A data assimilation technique are mainly located at upper troposphere. The two types of data can reconstruct the meso-scale information and improve the typhoon initial fields under themodel dynamic forcing effect, respectively from the low level and the upper level of atmosphere during the assimilating process. Numerical results show that with four-dimensional variational data assimilation (4DVAR) technique the circulation of initial fields is improved, the "warm core" of typhoon is enhanced, the "cloud water" phenomenon that occurs in the optimal initial fields and the numerical model is changed into "warm start" from "cold start". [ABSTRACT FROM AUTHOR]

Daily data from climate models submitted to the Fourth Assessment of the Intergovernmental Panel on Climate Change are compared with daily data from observations over Australia by measuring the overlap of the probability density functions (PDFs). The capacity of these models to simulate maximum temperature, minimum temperature, and precipitation is assessed. The resulting skill score is then used to exclude models with relatively poor skill region by region over Australia. The remaining sample of coupled climate models is then used to determine the seasonal changes in these three variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100. The authors demonstrate that some projected phenomena, such as the projected drying over southwest Western Australia, are robust and not caused by the inclusion of some weak models in earlier assessments. Some other results, such as the projected change in the monsoon, are more consistent among the good climate models. Consistent with earlier work, a consistent pattern of mean warming is identified in the projections. The amount of warming in the 99.7th percentile is not dramatically higher than the warming in the mean. However, while the mean warming is generally least in the south, the amount of warming in the 99.7th percentile is substantially higher along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall, which causes drying and allows extreme maximum temperatures to increase dramatically. The authors show that, in general, the amount of rainfall is projected to change relatively little, but the frequency of rainfall decreases and the intensity of rainfall at the upper tail of the distribution increases. However, the scale of the increase in extreme rainfall is not large on the time scales analyzed here. The range in projected temperature changes among those climate models with skill in simulating the observations is at least twice as large for the 99.7th/0.3rd percentiles as for the mean. For rainfall, the range among the good models is of order 10 times greater in the 99.7th percentile than in the mean. Since the impact of changes in extremes is increasingly recognized as societally important, this result strongly limits the use of climate model data to explore sectors that are vulnerable to extremes. This suggests an evaluation strategy that focuses on model capacity to simulate whole PDFs since capacity to simulate the mean is a necessary but insufficient criterion for determining a model’s value for future projection. [ABSTRACT FROM AUTHOR]

Garden biodiversity is an integral part of the urban ecosystem and play an important role in improving green infrastructure and aid in climate change adaptations. Little research is available on the floral diversity of the parks and gardens. The present study is an attempt to document the floral diversity of 77 gardens in Vadodara city and elucidate trends based on the size of the gardens. Study indicated presence of 217 species belonging to 72 families from the gardens of Vadodara city. The floristic data revealed that around 53% of the species represented indigenous species and 47% belonged to exotic species. Paper concluded that diversity is more in the small gardens as compared to large sized garden, where plantation of few species in undertaken. Species suitable to the semi-arid climate with low water requirement dominated the gardens indicative of better management practices and adapt to climate change. [ABSTRACT FROM AUTHOR]

An increase in the frequency of natural disasters around the world had caused a widespread attention to the issue of climate change. This research aimed to study the role of Community Based Disaster Risk Management approach in minimising these hazards by: 1) examining the level of commitment in the people of Lamphaya Subdistrict Municipality community towards Community Based Disaster Risk Management approach; and 2) examining the relationship between the 'prerequisites' and the success determined by preparedness, response, recovery, and mitigation. Through quantitative data analysis, the research revealed that the level of commitment are high in all aspects. Astoundingly, the data showed that the people of Lamphaya also felt the level of disaster management success is high in all aspects even though those same people (all but 1.9 percent) also reportedly claimed being 'moderately' and 'highly' affected by the disasters. Unlike what we learned from an extensive literature review, data from multiple regression model shows that all factors but 'stakeholder' significantly predict the success of Community Based Disaster Risk Management [ABSTRACT FROM AUTHOR]

Hailstorm research in tourism remains scant despite growing evidence of increasing hailstorms and associated damages to destinations and attractions due to climate change. This study examined hailstorm occurrence, damage and loss, preparedness and adaptation, recovery and resilience resilience-building at the Sun City Resort in South Africa. Data was collected from a staff survey (n=131), staff interviews, guest comments, and relevant Sun City Resort secondary documentation such as storm recovery and annual reports. It emerged that the 2018 hailstorm caused significant damages, yet no effective early warning systems were in place. This led to significant economic and non-economic losses, such as damage to infrastructure, cancelled activities and negative publicity. There was also no long-term strategy towards hailstorm resilience, with the resort adopting a reactive rather than proactive approach. The study contributes to climate change-induced hailstorm research in tourism while informing tourism destination resilience-building practices. Suggestions are made linked to building back and forth better in terms of hailstorm risk preparedness by the Sun City Resort. [ABSTRACT FROM AUTHOR]

BACKGROUND: Recently enacted environmental justice policies in the United States at the state and federal level emphasize addressing place-based inequities, including persistent disparities in air pollution exposure and associated health impacts. Advances in air quality measurement, models, and analytic methods have demonstrated the importance of finer-scale data and analysis in accurately quantifying the extent of inequity in intraurban pollution exposure, although the necessary degree of spatial resolution remains a complex and context-dependent question. OBJECTIVE: The objectives of this commentary were to a) discuss ways to maximize and evaluate the effectiveness of efforts to reduce air pollution disparities, and b) argue that environmental regulators must employ improved methods to project, measure, and track the distributional impacts of new policies at finer geographic and temporal scales. DISCUSSION: The historic federal investments from the Inflation Reduction Act, the Infrastructure Investment and Jobs Act, and the Biden Administration's commitment to Justice40 present an unprecedented opportunity to advance climate and energy policies that deliver real reductions in pollution-related health inequities. In our opinion, scientists, advocates, policymakers, and implementing agencies must work together to harness critical advances in air quality measurements, models, and analytic methods to ensure success. [ABSTRACT FROM AUTHOR]

Research on multi-dimensional aspect of dam engineering is gaining momentum because of massive flood destructions in lives, ecosystem and development including Wivenhoe dam flood in 2011, Oroville dam's spillway incident 2017 and the Europe's widespread flooding in 2021. The aim of this study is to capture and review research activities in dam science since 1980. A case study of the Wivenhoe dam Brisbane Australia, focusing on its design, catchment, water storage and flood mitigation capacity, management procedure, climate change and historical rainfall pattern has been carried out to conclude why such a catastrophic flood event happened in Brisbane in January 2011. The methodology applied in the study includes related literature review followed by documentation and analysis of reports and data of Queensland Water, Wivenhoe Alliance and Flood Commission. Reviewed literatures indicated that dam failures are primarily associated with improper design, inadequate monitoring in construction period and poor management (operational) practices. In the case of the Wivenhoe dam flood, the report of the Flood Commission of Inquiry Australia was that the dam was operated so that its flood mitigation was near optimal. Whilst the operators were found to be at fault for not following the Operation Manual, it was found that the manual was confusing and difficult to follow, and therefore, they were cleared of all liability. Hence, it is difficult to conclude what would have actually happened if the Wivenhoe dam operators had released more water earlier, author's reasoned outlook about the flood mitigation measures used at the time is to ask the question "was reasonable discretion" used during the flood. In the light of the warnings that the dam operators were given even as far back as December 2010 about the strong La Nina, it would seem that operators made sub-optimal decision about water releases and hence "reasonable discretion" wasn't applied properly. Hence, it can be said that the Brisbane Flood 2011 was a dam release type flood basically related to poor management. [ABSTRACT FROM AUTHOR]

The region situated along the southern coast of Papua Island exhibits a considerable likelihood of upwelling, which is distinguished by higher amounts of chlorophyll-a concentration. The present study examined the variability of upwelling phenomena spanning a period of 25 years (1998-2022) through the utilization of satellite-derived data obtained from the Ocean-Colour Climate Change Initiative (OC-CCI). Apart from the chlorophyll-a concentration, the southern region of Papua exhibits a significant amount of suspended sediment contamination, as indicated by the observations of Rrs 555. Upon conducting EOF analysis during each season, it has been observed that there has been a significant rise in the levels of chlorophyll-a concentrations over the past several years in the northern region of the Arafura Sea. The period spanning from 2016 to 2022 witnessed a rise in precipitation amounts, leading to a greater transportation of nutrients through water discharge and consequently resulting in an increase of chlorophyll-a concentration. [ABSTRACT FROM AUTHOR]

Current political discussions and developments indicate the importance and urgency of incorporating climate change considerations into EIA processes. The recent revision of the EU Directive 2014/52/EU on Environmental Impact Assessment (EIA) requires changes in the EIA practice of the EU member states. This paper investigates the extent to which the Environmental Impact Assessment (EIA) can contribute to an early consideration of climate change consequences in planning processes. In particular the roles of different actors in order to incorporate climate change impacts and adaptation into project planning subject to EIA at the appropriate levels are a core topic. Semi-structured expert interviews were carried out with representatives of the main infrastructure companies and institutions responsible in these sectors in Austria, which have to carry out EIA regularly. In a second step expert interviews were conducted with EIA assessors and EIA authorities in Austria and Germany, in order to examine the extent to which climate-based changes are already considered in EIA processes. This paper aims to discuss the different perspectives in the current EIA practice with regard to integrating climate change impacts as well as barriers and solutions identified by the groups of actors involved, namely project developers, environmental competent authorities and consultants (EIA assessors/practitioners). The interviews show that different groups of actors consider the topic to different degrees. Downscaling of climate change scenarios is in this context both, a critical issue with regards to availability of data and costs. Furthermore, assistance for the interpretation of relevant impacts, to be deducted from climate change scenarios, on the specific environmental issues in the area is needed. The main barriers identified by the EIA experts therefore include a lack of data as well as general uncertainty as to how far climate change should be considered in the process without reliable data but in the presence of knowledge about possible consequences at an abstract level. A joint strategy on how to cope with uncertain prognoses about main impacts on environmental issues for areas without reliable data requires a discussion and cooperation between EIA consultants and environmental authorities. [ABSTRACT FROM AUTHOR]

Large-scale climate history of the past millennium reconstructed solely from tree-ring data is prone to underestimate the amplitude of low-frequency variability. In this paper, we aimed at solving this problem by utilizing a novel method termed “MDVM”, which was a combination of the ensemble empirical mode decomposition (EEMD) and variance matching techniques. We compiled a set of 211 tree-ring records from the extratropical Northern Hemisphere (30–90°N) in an effort to develop a new reconstruction of the annual mean temperature by the MDVM method. Among these dataset, a number of 126 records were screened out to reconstruct temperature variability longer than decadal scale for the period 850–2000 AD. The MDVM reconstruction depicted significant low-frequency variability in the past millennium with evident Medieval Warm Period (MWP) over the interval 950–1150 AD and pronounced Little Ice Age (LIA) cumulating in 1450–1850 AD. In the context of 1150-year reconstruction, the accelerating warming in 20th century was likely unprecedented, and the coldest decades appeared in the 1640s, 1600s and 1580s, whereas the warmest decades occurred in the 1990s, 1940s and 1930s. Additionally, the MDVM reconstruction covaried broadly with changes in natural radiative forcing, and especially showed distinct footprints of multiple volcanic eruptions in the last millennium. Comparisons of our results with previous reconstructions and model simulations showed the efficiency of the MDVM method on capturing low-frequency variability, particularly much colder signals of the LIA relative to the reference period. Our results demonstrated that the MDVM method has advantages in studying large-scale and low-frequency climate signals using pure tree-ring data. [ABSTRACT FROM AUTHOR]

Indicator species (IS) are used to monitor environmental changes, assess the efficacy of management, and provide warning signals for impending ecological shifts. Though widely adopted in recent years by ecologists, conservation biologists, and environmental practitioners, the use of IS has been criticized for several reasons, notably the lack of justification behind the choice of any given indicator. In this review, we assess how ecologists have selected, used, and evaluated the performance of the indicator species. We reviewed all articles published in Ecological Indicators (EI) between January 2001 and December 2014, focusing on the number of indicators used (one or more); common taxa employed; terminology, application, and rationale behind selection criteria; and performance assessment methods. Over the last 14 years, 1914 scientific papers were published in EI, describing studies conducted in 53 countries on six continents; of these, 817 (43%) used biological organisms as indicators. Terms used to describe organisms in IS research included “ecological index”, “environmental index”, “indicator species”, “bioindicator”, and “biomonitor,” but these and other terms often were not clearly defined. Twenty percent of IS publications used only a single species as an indicator; the remainder used groups of species as indicators. Nearly 50% of the taxa used as indicators were animals, 70% of which were invertebrates. The most common applications behind the use of IS were to: monitor ecosystem or environmental health and integrity (42%); assess habitat restoration (18%); and assess effects of pollution and contamination (18%). Indicators were chosen most frequently based on previously cited research (40%), local abundance (5%), ecological significance and/or conservation status (13%), or a combination of two or more of these reasons (25%). Surprisingly, 17% of the reviewed papers cited no clear justification for their choice of indicator. The vast majority (99%) of publications used statistical methods to assess the performance of the selected indicators. This review not only improves our understanding of the current uses and applications of IS, but will also inform practitioners about how to better select and evaluate ecological indicators when conducting future IS research. [ABSTRACT FROM AUTHOR]