Search Results (101)

The primary objective of this paper is to quantify a realistic pathway for Canada to reach net-zero emissions by 2050. This study analyzed greenhouse gas (GHG) emissions from the 10 provinces and 3 territories of Canada based on the emissions from their economic sectors. A time series analysis was performed to understand the trajectory of the emissions profile from 1990 to 2023. Using the 2023 emissions as the baseline, a linear reduction, based on the GHG proportions from each jurisdiction, was performed and projected to 2050 (except for Prince Edward Island (PEI), where net zero was targeted for 2040). Moreover, a machine learning technique (k-means unsupervised algorithm) was used to group all the jurisdictions into homogeneous regions for national strategic climate policy initiatives. The within-cluster sum of squares identified the following clusters: Cluster 1: Manitoba (MB), New Brunswick, Nova Scotia, and Newfoundland and Labrador; Cluster 2: Alberta (AB); Cluster 3: Quebec (QC) and Saskatchewan; Cluster 4: Ontario (ON); and Cluster 5: PEI, Northwest Territories, Nunavut, and Northwest Territories. Considering the maximum GHG reductions needed per cluster (Clusters 1–5), the results show that 0.309 Mt CO₂ eq/year, 5.447 Mt CO₂ eq/year, 1.293 Mt CO₂ eq/year, 2.217 Mt CO₂ eq/year, and 0.04 Mt CO₂ eq/year must be targeted from MB (transportation), AB (stationary combustion), QC (transportation), ON (stationary combustion) and PEI (transportation), respectively. The concept of climate stabilization wedges, which provides a practical framework for addressing the monumental challenge of mitigating climate change, was introduced to each derived region to cut GHG emissions in Canada through tangible, measurable actions that is specific to each sector/cluster. The clustering-based method breaks climate mitigation problems down into manageable pieces by grouping the jurisdictions into efficient regions that can be managed effectively by fostering collaboration across jurisdictions and economic sectors. Actionable and strategic recommendations were made within each province to reach the goal of net-zero. The implications of this study for policy and climate action include the fact that actionable strategies and tailored policies are applied to each cluster’s emission profile and economic sector, ensuring equitable and effective climate mitigation strategies in Canada. Full article

This paper presents the results of studies on intra-annual runoff changes in the Ile River basin based on data from gauging stations up to 2021. Changes in climatic characteristics that determine runoff formation in the mountainous and foothill areas of the river catchment have led to alterations in the water regime of the watercourses. The analysis of the temporal and spatial patterns of river flow formation in the basin, as well as its distribution by seasons and months, is essential for solving applied water management problems and assessing the risks of hazardous hydrological phenomena, such as high floods and low water levels. The statistical analysis of annual and monthly river runoff fluctuations enabled the identification of relatively homogeneous estimation periods during stationary observations under varying climatic conditions. The obtained characteristics of annual and intra-annual river runoff in the Ile River basin for the modern period provide insights into changes in average monthly water discharge and, more broadly, runoff volume during different phases of the water regime. In the future, these characteristics are expected to guide the design of hydraulic structures and the rational use of surface runoff in this intensively developing region of Kazakhstan. Full article

High-resolution groundwater storage is essential for effective regional water resource management. While Gravity Recovery and Climate Experiment (GRACE) satellite data offer global coverage, the coarse spatial resolution (0.25–0.5°) limits the data’s applicability at regional scales. Traditional downscaling methods often fail to effectively capture spatial heterogeneity within regions, leading to reduced model performance. To overcome this limitation, a zoned downscaling strategy based on time series clustering is proposed. A K-means clustering algorithm with dynamic time warping (DTW) distance, combined with a random forest (RF) model, was employed to partition the Hexi Corridor region into relatively homogeneous subregions for downscaling. Results demonstrated that this clustering strategy significantly enhanced downscaling model performance. Correlation coefficients rose from 0.10 without clustering to above 0.84 with K-means clustering and the RF model, while correlation with the groundwater monitoring well data improved from a mean of 0.47 to 0.54 in the first subregion (a) and from 0.40 to 0.45 in the second subregion (b). The driving factor analysis revealed notable differences in dominant factors between subregions. In the first subregion (a), potential evapotranspiration (PET) was found to be the primary driving factor, accounting for 33.70% of the variation. In the second subregion (b), the normalized difference vegetation index (NDVI) was the dominant factor, contributing 29.73% to the observed changes. These findings highlight the effectiveness of spatial clustering downscaling methods based on DTW distance, which can mitigate the effects of spatial heterogeneity and provide high-precision groundwater monitoring data at a 1 km spatial resolution, ultimately improving water resource management in arid regions. Full article

This research evaluates the rheological and mechanical properties of polymer- and nanomaterials-modified bitumen by incorporating nanosilica (NSA), nanoclay (NCY), and Acrylonitrile Styrene Acrylate (ASA) at 5% by weight of the bitumen. The samples were prepared at 165 °C for one hour to obtain homogeneous blends. All samples were subjected to short- and long-term aging to simulate the effects of different operating conditions. The research conducted a series of tests, including consistency, frequency sweep, and multiple creep stress and recovery (MSCR) using the dynamic shear rheometer (DSR) and bending beam rheometer (BBR). The results showed that all modified bitumen outperformed the neat bitumen. The frequency sweep showed a higher complex modulus (G*) and lower phase angle (δ), indicating enhanced viscoelastic properties and, thus, higher resistance to permanent deformation. The BBR test revealed that the bitumen modified with NCY5% has a creep stiffness of 47.13 MPa, a 51.5% improvement compared to the neat bitumen, while the NSA5% has the highest m-value, a 28.5% enhancement compared with the neat bitumen. The MSCR showed that the modified blends have better recovery properties and, therefore, better resistance to permanent deformation under repeated loadings. The aging index demonstrated that the modified bitumen is less vulnerable to aging and maintains their good flexibility and resistance to permanent deformations. Finally, these results showed that adding 5% polymer and nanomaterials improved the bitumen’s’ performance before and after aging by reducing permanent deformation and enhancing crack resistance at low temperatures, thus extending the pavement service life and making them an effective alternative for improving pavement performance in various climatic conditions and under high traffic loads. Full article

Time series data are fundamental for analyzing temporal dynamics and patterns, enabling researchers and practitioners to model, forecast, and support decision-making across a wide range of domains, such as finance, climate science, environmental studies, and signal processing. In the context of high-dimensional time series, the Vector Autoregressive model (VAR) is widely used, wherein each variable is modeled as a linear combination of lagged values of all variables in the system. However, the traditional VAR framework relies on the assumption of stationarity, which states that the autoregressive coefficients remain constant over time. Unfortunately, this assumption often fails in practice, especially in systems subject to structural breaks or evolving temporal dynamics. The Time-Varying Vector Autoregressive (TV-VAR) model has been developed to address this limitation, allowing model parameters to vary over time and thereby offering greater flexibility in capturing non-stationary behavior. In this study, we propose an enhanced modeling approach for the TV-VAR framework by incorporating minimization solvers in generalized additive models and one-sided kernel smoothing techniques. The effectiveness of the proposed methodology is assessed using simulations based on non-homogeneous Markov chains, accompanied by a detailed discussion of its advantages and limitations. Finally, we illustrate the practical utility of our approach using an application to real-world financial data. Full article

The aim of the homogenization of climatic time series is to remove non-climatic biases from the observed data, which are caused by technical or environmental changes during the period of observations. This bias removal is generally more successful for long-term trends and annual means than for monthly and daily values. The homogenization of probability distribution (HPD) may improve data accuracy even for daily data when the signal-to-noise ratio favors its application. HPD can be performed by quantile matching or spatial interpolations, but both of them have drawbacks. This study presents a new algorithm which helps to increase homogenization accuracy in all temporal and spatial scales. The new method is similar to quantile matching, but section mean values of the probability distribution function (PDF) are compared instead of individual daily values. The input dataset of the algorithm is identical with the homogenization results for section means of the studied time series. The algorithm decides about statistical significance for each break detected during the homogenization of the section means, and skips the insignificant breaks. Correction terms for removing the inhomogeneity biases of PDF are calculated jointly by a Benova-like equation system, a low pass filter is used for smoothing the prime results, and the mean value of the input time series between two consecutive detected breaks is preserved for each of such sections. This initial version does not deal with seasonal variations either during HPD or in other steps of the homogenization. The method has been tested connecting HPD to ACMANTv5.3, and using overall 8 wind speed and relative humidity datasets of the benchmark of European project INDECIS. The results show 4 to 12 percent RMSE reduction by HPD in all temporal scales, except for the extreme tails where a part of the results are weaker. Full article

The European Green Deal emphasizes the development of renewable energy sources to combat climate change. However, as photovoltaic expansion accelerates, so does the potential for increased waste, necessitating effective material recycling strategies. Indium, a scarce and valuable element crucial to the production of photovoltaic panels, underscores the necessity for efficient recycling practices to reduce reliance on virgin resources. In a recent laboratory analysis, a CIGS photovoltaic panel underwent a series of processes including crushing, grinding, and homogenization. The concentration of indium, vital for recycling, was meticulously analyzed using ICP-MS and validated through microscopic and composition analyses. Subsequent extraction utilizing 3 M HCl and H₂O₂, followed by electrolysis, yielded a remarkable up to 52% indium recovery within a 48-h timeframe. Importantly, the study encompassed both averaged panel samples and samples from the absorbing layer, emphasizing the comprehensive approach required for efficient recycling. This underscores the critical importance of optimizing recycling processes to mitigate the environmental impact associated with the disposal of photovoltaic panels. By maximizing indium recovery, not only are environmental impacts reduced, but the long-term sustainability of renewable energy technologies is also ensured. This highlights the interconnectedness of recycling practices with the broader goals of achieving a circular economy and securing the viability of renewable energy systems in the fight against climate change. Full article

In this research, based on a case study of Ukraine, we (1) examined the sensitivity of heatwave (HW) climatology to input gridded data and (2) statistically compared HW metrics (such as duration, intensity, etc.) calculated from the gridded data against similar results derived from high-quality station time series. For the first task, we used a mini statistical ensemble of gridded datasets of the daily maximum air temperature (TX). The ensemble included the following: ClimUAd and E-OBS (Ukrainian and European observation-based gridded data, respectively), reanalyzes ERA5, ERA5-Land, NOAA-CIRES 20CR V2c and V3, and NCEP-NCAR R1. For the second task, the same gridded data were used along with 178 quality-controlled and homogenized TX station time series from Ukraine. HWs and their metrics were defined according to the approach summarized by Perkins and Alexander (2013). All calculations were performed for the period 1950–2014. Our results showed that, depending on the gridded dataset, the calculated values of HW metrics might differ significantly. Even after averaging over the study period and the territory of Ukraine, the ranges between the max and min values of HW metrics remain large. For instance, the spread in HW number per year may be up to six events. However, the differences in the trend slopes of HW metrics are less pronounced. In addition, the comparison of HW calculations derived using gridded and station data showed that E-OBS, ERA5, and ERA5-Land provide similar verification statistics. The evaluation statistics for 20CRV3 are worse compared to E-OBS, ERA5, and ERA5-Land, but significantly better than for 20CRV2c and NCEP-NCAR R1. Our findings can aid in selecting gridded datasets for calculating reliable HW climatology and, consequently, contribute to developing climate adaptation strategies for extreme temperature events in Ukraine, its neighboring countries, and potentially across Europe. Full article

The Dobrogea region, located in southeastern Romania, experiences a semi-arid climate. This study provides a deep analysis of monthly precipitation series from 46 meteorological stations spanning 1965–2005, exploring mean and variance characteristics and detecting structural changes in precipitation patterns. The series normality was assessed using the Lilliefors test, and transformation, such as the Yeo–Johnson method, was used to address skewness. Analyses of mean and variance included parametric (t-tests, ANOVA) and non-parametric (Mann–Whitney U, Fligner–Killeen) tests to address the homogeneity/inhomogeneity of the data series in mean and variance. Change points were detected using a Minimum Description Length (MDL) framework, modeling the series as piecewise linear regressions with seasonal effects and autocorrelated errors. Pairwise comparisons indicate the low similarity of the series means, and variances, so spatial and temporal variability in precipitation is notable. Validation of the proposed MDL approach on synthetic datasets demonstrated high accuracy, and application to real data identified significant shifts in precipitation regimes. Applied to the monthly series collected at the ten main hydro-meteorological stations, a MDL framework provided at least two change points for each. Full article

Currently, ground-based global navigation satellite system (GNSS) techniques have become widely recognized as a reliable and effective tool for atmospheric monitoring, enabling the retrieval of zenith total delay (ZTD) and precipitable water vapor (PWV) for meteorological and climate research. The International GNSS Service analysis centers (ACs) have initiated their third reprocessing campaign, known as IGS Repro3. In this campaign, six ACs conducted a homogeneous reprocessing of the ZTD time series spanning the period from 1994 to 2022. This paper primarily focuses on ZTD products. First, the data processing strategies and station conditions of six ACs were compared and analyzed. Then, formal errors within the data were examined, followed by the implementation of quality control processes. Second, a combination method is proposed and applied to generate the final ZTD products. The resulting combined series was compared with the time series submitted by the six ACs, revealing a mean bias of 0.03 mm and a mean root mean square value of 3.02 mm. Finally, the time series submitted by the six ACs and the combined series were compared with VLBI data, radiosonde data, and ERA5 data. In comparison, the combined solution performs better than most individual analysis centers, demonstrating higher quality. Therefore, the advanced method proposed in this study and the generated high-quality dataset have considerable implications for further advancing GNSS atmospheric sensing and offer valuable insights for climate modeling and prediction. Full article

Precipitation is crucial for water resource renewal, but climate change alters their frequency and amounts, challenging societies for correct and effective water management. However, modifications of precipitation dynamics appear to be not uniformly distributed, both in space and time. Even in relatively small areas, precipitation shows the coexistence of positive and negative trends. Local topography seems to be a strong driver of precipitation changes. Understanding precipitation changes and their relationship with local topography is crucial for society’s resilience. Taking advantage of a dense and long-lasting (1920–2019) meteorological monitoring network, we analyzed the precipitation changes over the last century in a sensitive and strategic area in the Mediterranean hotspot. The study area corresponds to northern Tuscany (Italy), where its topography comprises mountain ridges and coastal and river plains. Forty-eight rain gauges were selected with continuous annual precipitation time series. These were analyzed for trends and differences in mean annual precipitation between the stable period of 1921–1970 and the last 30-year 1990–2019. The relationship between precipitation changes and local topography was also examined. The results show the following highlights: (i) A general decrease in precipitation was found through the century, even if variability is marked. (ii) The mountain ridges show the largest decrease in mean annual precipitation. (iii) The precipitation change entity over the last century was not homogenous and was dependent on topography and geographical setting. (iv) A decrease in annual precipitation of up to 400 mm was found for the mountainous sites. Full article

This experimental study evaluated the performance of modified asphalt mixtures prepared by incorporating 2%, 4%, and 6% linear low-density polyethylene (LLDPE) by weight of asphalt binder through a series of tests. The microstructural analyses using scanning electron microscopy (SEM) were conducted on asphalt samples to assess the engineering properties of the asphalt mixes. Finally, ANOVA statistical analysis has been employed to determine the statistical significance of the differences in all tests’ means. Based on laboratory findings, the Marshall stability test result showed that the modified asphalt mixes up to 4% LLDPE had enhanced performance by 12.7% compared to the control mix. A significant decrease (up to 31.3%) in binder penetration was demonstrated due to the incorporation of LLDPE into the asphalt mix. The softening point of the LLDPE–asphalt mixes was increased by up to 17.6%. It was also demonstrated that the incorporation of such LLDPE dosages maintains the flow limits within the specified range; however, the flow of the asphalt mix with 4% LLDPE was 3.17 mm which is the nearest to the average value of the upper and lower acceptable limits. The air voids of mixes with LLDPE content more than 4% by was decreased to less than 4% which is not recommended in high-temperature climates to control mixture bleeding. Microscopic analysis revealed an improvement in the densification of asphalt microstructures, attributed to the LLDPE particles significantly changing the rheology and viscosity of the base mixture and making the hot asphalt mixture more homogeneous. Based on the physical and rheological properties investigated in this study, it could be concluded that 4% LLDPE produces the best performance in asphalt mixtures. Overall, the ANOVA analysis demonstrated that the incorporation of LLDPE into asphalt mixes has a significant impact on all of their properties. Full article

Energy consumption demand has shown successive records during recent months, primarily associated with heat waves in almost all Brazilian states. The effects of climate change induced by global warming and the increasingly frequent occurrence of extreme events, mainly regarding temperature and precipitation, are associated with this increase in demand. In this sense, the impact of meteorological variables on load demand in some substations in the northeast and southeast of Brazil was analyzed, considering the historical series of energy injected into these substations. Fifteen substations were analyzed: three in the state of São Paulo, six in Bahia, three in Pernambuco, and three in Rio Grande do Norte. Initially, essential quality control was carried out on the energy injection data. The SAMeT data sets were used for the variable temperature, and Xavier was used for precipitation and relative humidity to obtain a homogeneous data series. Daily and monthly data were used for a detailed analysis of these variables in energy demand over the northeast and southeast regions of Brazil. Some regions were observed to be sensitive to the maximum temperature variable, while others were sensitive to the average temperature. On the other hand, few cases showed the highest correlation with the precipitation and relative humidity variables, with most cases being considered slight or close to zero. A more refined analysis was based on the type of consumers associated with each substation. These results showed that where consumption is more residential, the highest correlations were associated with maximum temperature in most stations in the northeast and average temperature in the southeast. In regions where consumption is primarily rural, the correlation observed with precipitation and relative humidity was the highest despite being negative. A more detailed analysis shows that rural production is associated with irrigation in these substations, which partly explains consumption, as when rainfall occurs, the demand for irrigation decreases, and thus energy consumption is reduced. Full article

Clouds are key to understanding the atmosphere and climate, and a long series of satellite observations provide invaluable information to study their properties. EUMETSAT has published Release 1 of the Optimal Cloud Analysis (OCA) Climate Data Record (CDR), which provides a homogeneous time series of cloud properties of up to two overlapping layers, together with uncertainties. The OCA product is derived using the 15 min Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements onboard Meteosat Second Generation (MSG) in geostationary orbit and covers the period from 19 January 2004 until 31 August 2019. This paper presents the validation of the OCA cloud-top pressure (CTP) against independent lidar-based estimates and the quality assessment of the cloud optical thickness (COT) and cloud particle effective radius (CRE) against a combination of products from satellite-based active and passive instruments. The OCA CTP is in good agreement with the CTP sensed by lidar for low thick liquid clouds and substantially below in the case of high ice clouds, in agreement with previous studies. The retrievals of COT and CRE are more reliable when constrained by solar channels and are consistent with other retrievals from passive imagers. The resulting cloud properties are stable and homogeneous over the whole period when compared against similar CDRs from passive instruments. For CTP, the OCA CDR and the near-real-time OCA products are consistent, allowing for the use of OCA near-real time products to extend the CDR beyond August 2019. Full article

Homogenization of the time series of observed climatic data aims to remove non-climatic biases caused by technical changes during the history of the climate observations. The spatial redundancy of climate information helps to recognize station-specific inhomogeneities with statistical methods, but the correct detection and removal of inhomogeneity biases is generally not easy for the combined effects of individual inhomogeneities. In a homogenization procedure, several time series of a given climatic variable observed in one climatic region are usually homogenized together via a large number of spatial comparisons between them. Such procedures are called relative homogenization. A relative homogenization procedure may include one or more homogenization cycles where a cycle includes the steps of time series comparison, inhomogeneity detection and corrections for inhomogeneities, and they may include other steps like the filtering of outlier values or spatial interpolations for infilling data gaps. Relative homogenization methods differ according to the number and content of the individual homogenization cycles, the procedure for the time series comparisons, the statistical inhomogeneity detection method, the way of the inhomogeneity bias removal, among other specifics. Efficient homogenization needs the use of tested statistical methods to be included in partly or fully automated homogenization procedures. Due to the large number and high variety of homogenization experiments fulfilled in the Spanish MULTITEST project (2015–2017), its method comparison test results are still the most informative about the efficiencies of homogenization methods in use. This study presents a brief review of the advances in relative homogenization, recalls some key results of the MULTITEST project, and analyzes some theoretical aspects of successful homogenization. Full article