Trend Detection Research Articles

Attribution assessment of hydrological trends and extremes to climate change for Northern high latitude catchments in Norway

The northern high latitudes have experienced the strongest warming in the world and substantial changes in streamflow and hydrological extremes. However, there have been limited attribution studies of changes in streamflow and hydrological extremes in this region. This study provides the first trend detection and attribution assessment on 33 hydrological variables for 50 Norwegian catchments in the period 1961–2019, using observed and simulated runoff data from four hydrological models driven by factual (observed) and counterfactual forcing data. Significant increasing trends are detected in observed annual, spring and winter runoff in most catchments and significant trends towards earlier spring floods are found in 40% of catchments. The four hydrological models show similarly good performance in terms of daily discharge in both calibration and validation periods, and they can reproduce 62% of the observed significant trends considering both trend direction and significance. The counterfactual forcing data were generated by the ATTRICI model, which removed all warming trends and most significant trends in precipitation in the factual time series. Ninety-four percent of the simulated significant trends driven by the factual forcing data are insignificant under counterfactual conditions, with trend slopes approaching zero. Thus, based on the model performance in trend reproduction and the difference of significant trends under factual and counterfactual conditions, we conclude that about 58% of the observed significant trends in Norwegian catchments can be attributed mainly to climate change. The comparisons of the historical extreme events under factual and counterfactual conditions show that more than 65% of floods and droughts in the 2010s could have been magnified by climate change.

Serum prostate-specific antigen trends and prostate cancer detection on follow-up in men with a prior negative biopsy: A cohort study

ABSTRACT Introduction: Elevated serum prostate-specific antigen (PSA) is the most common trigger for a prostate biopsy. However, the range of normal PSA is poorly defined in many populations. Men with “elevated” PSA may not harbor cancer, and it is unclear if such men with a prior negative prostate biopsy should be biopsied again. We conducted a cohort study to assess the PSA trends and cancer detection rates in such men. Methods: In an Institutional Review Board-approved ambispective study, men who underwent prostate biopsy between January 2016 and December 2021 for PSA > 4 ng/mL were identified. Among them, those whose biopsy was negative for malignancy were contacted either telephonically or reviewed in person, and the most recent PSA and histopathology of any repeat prostate biopsy were determined. These were evaluated to assess the PSA trend, re-biopsy rate, and cancer detection rate. Results: During the study period, prostate biopsies were performed in a total of 1260 men; out of which 444 were negative for malignancy and 241 patients fulfilled the inclusion criteria. Their median prebiopsy PSA was 9.81 ng/mL (interquartile range [IQR]: 7.14–15.6), and the median follow-up PSA was 5.08 (IQR: 3.18–8.4). At a median follow-up of 53 months (range: 6–77 months), PSA had decreased in 177 (73.4%) patients, was static in 48 (19.9%) patients, and increased in only 16 (6.6%) patients. Repeat biopsy was performed on 20 patients; of whom seven had cancer (35%) with an overall positivity rate of 2.9% among the 241 patients. Although the positivity rate was higher in men with increased PSA, it was not statistically different from those with lower or similar PSA. No factors could be identified to predict a positive repeat biopsy. Conclusions: PSA, the sole trigger for a prostate biopsy, declined in nearly three-quarters of men with a negative first biopsy, and <3% of men were detected to have cancer on a repeat biopsy. This information could help appropriately counsel patients and allay anxiety after a negative biopsy.

Challenges in the Detection and Attribution of Northern Hemisphere Surface Temperature Trends Since 1850

Since 2007, the Intergovernmental Panel on Climate Change (IPCC) has heavily relied on the comparison between global climate model hindcasts and global surface temperature (ST) estimates for concluding that post-1950s global warming is mostly human-caused. In Connolly et al., we cautioned that this approach to the detection and attribution of climate change was highly dependent on the choice of Total Solar Irradiance (TSI) and ST data sets. We compiled 16 TSI and five ST data sets and found by altering the choice of TSI or ST, one could (prematurely) conclude anything from the warming being “mostly human-caused” to “mostly natural.” Richardson and Benestad suggested our analysis was “erroneous” and “flawed” because we did not use a multilinear regression. They argued that applying a multilinear regression to one of the five ST series re-affirmed the IPCC’s attribution statement. They also objected that many of the published TSI data sets were out-of-date. However, here we show that when applying multilinear regression analysis to an expanded and updated data set of 27 TSI series, the original conclusions of Connolly et al. are confirmed for all five ST data sets. Therefore, it is still unclear whether the observed warming is mostly human-caused, mostly natural or some combination of both.

Challenge of Detecting Personal Deviations and Trends in Sensor Based Activity Data.

Physical activity and health are closely linked. Therefore, monitoring movement behavior is of great interest e.g., to monitor a patient's physical state. Nowadays it is easy to record movement with a smartphone. The aim of this work was to develop a concept to detect trends based on personalized movement behavior recorded with a smartphone. A first prototype with a control chart was designed. Since this approach did not prove suitable for analyzing activity data for trends in practice, a second prototype was subsequently developed with a statistical trend test (Mann-Kendall test (MK test)). It was extended by the Yue-Wang correction approach to be able to deal effectively with serial correlation. Furthermore, the traditional trend modeling using Theil-Sen slope was extended by three additional models to be able to represent non-linear trend shapes. Movement behavior can be highly variable, which leads to wide control limits when using control charts. As the lower control limit was always in the negative range the use of a control chart was impossible for this use case. The evaluation results of the second prototype confirm the choice of a non-parametric test, as well as the decision for the Yue-Wang correction factor. Furthermore, it could be determined that the MK test is robust against outliers. The number of detected trends increases with increasing significance level. The MK test is also suitable for detecting step-like trends. Live trend detection is not straightforward with the MK test but can be simulated by overlapping time periods. In the future, trend modeling should be extended even further, as it plays a major role in the concept. The sensitivity of the test can be increased by means of various parameters.

Artificial intelligence trend analysis on healthcare podcasts using topic modeling and sentiment analysis: a data-driven approach

Over the past few decades, the topic of artificial intelligence (AI) has gained considerable attention in both research and industry. In particular, the healthcare sector has witnessed a surge in the use of AI applications, as the maturity of these methods increased. However, as the use of machine learning (ML) in healthcare continues to grow, we believe it will become increasingly important to examine public perceptions of this trend to identify potential impediments and future directions. Current work focuses mainly on academic data sources and industrial applications of AI. However, to gain a comprehensive understanding of the increased societal interest in AI, digital media such as podcasts should be consulted, as they are accessible to a broader audience. In order to examine this hypothesis, we investigate the AI trend development in healthcare from 2015 until 2021. In this study, we propose a web mining approach to collect a novel data set consisting of 29 healthcare podcasts with 3449 episodes. We identify 102 AI-related buzzwords that were extracted from various glossaries and hype cycles. These buzzwords were used to conduct an extensive trend detection and analysis study on the collected data using machine learning-based approaches. We successfully detect an AI trend and follow its evolution in healthcare podcasts over several years. Besides the focus area of AI, we are able to detect 14 topic clusters and visualize the trending or decreasing dominant topics over the whole period under consideration. In addition, we analyze the sentiments in podcasts towards the identified topics and deliver further insights for trend detection in healthcare. Finally, the collected data set can be used for trend detection besides AI-related topics using topic clustering.

Trend Detection and Adaptive Removal in Pressure Signal for Pipeline Leak Monitoring

Trend Detection and Adaptive Removal in Pressure Signal for Pipeline Leak Monitoring

Environmental impacts of landfills: perspectives on bio-monitoring

Environmental regulations on landfills contain detailed instructions for monitoring the impacts of pollution from leachate on water, air and soil. However, references to the impact of landfills on the landscape and the need to monitor the surrounding vegetation are described only in general terms. Studies have indicated that near-surface pollution events, which are not necessarily captured by existing regulatory monitoring schemes, have affected the vegetation in the vicinity of landfills. Indications for the effects of pollution emanating from landfills include the retreat of sensitive and native plant species, the abundance of halophytes or nitrophilous plants and the prevalence of other invasive plant species, which can spread to adjacent ecosystems. To the best of the authors’ knowledge, a comprehensive synthesis of landfill plant-based biomonitoring results has not yet been reported. The advantage of biomonitoring lies in its ability to assess the quality of the environment as perceived by living organisms. This would facilitate the determination of the response of plants to departures from natural conditions, detection of trends occurring in ecosystems and adoption of management practices to prevent or mitigate degradation of the environment. Thus, to detect such effects on the flora surrounding a landfill, this paper recommends that biomonitoring be utilised in environmental regulations to complement existing monitoring techniques.

Citizen involvement in reducing end-of-life product waste in Mexico City

Citizen involvement initiatives are being implemented to facilitate the collection of end-of-life returns and to assist local governments in reducing waste generation and diverting waste from landfills. Three initiatives in Mexico City have been significant for many years in diverting recyclables, e-waste, and batteries from landfills: the comprehensive deposit return system (CDRE), e-waste donation and recycling (EWDR), and deposit batteries for diversion (DBD). Previous studies have generally described Mexico City's solid waste management practices and influencing factors, but they have not explored the relevance of the initiatives for waste diversion. Motivated by this, this study evaluated the impact of the citizen involvement initiatives from 2007 to 2021 using official Mexico City government data for 189 campaigns. An overview of the initiatives was presented, followed by assessing their yield by waste type and region, trend detection of waste diversion data using linear regression and the non-parametric Mann-Kendall tests, carbon emission reductions, and determining the challenges and future actions. The findings indicated that: (1) the annual average waste diversion potential reached up to 263.63 t, reducing carbon emissions by approximately 304.02 t CO2-eq; (2) the waste diversion has progressively grown by 65 %, with a significant upward trend; (3) recyclable electrical and electronic waste were the most abundant fraction, accounting for 63 % of the total, and packaging waste was the least; and (4) the contribution of the EWDR collection initiative in waste diversion was higher than CDRE and DBD. Finally, we noticed that despite their success, the total quantity of resources collected represents only a small portion of citywide recycling, necessitating large-scale local campaign deployments for improved waste diversion. These findings can assist waste management stakeholders in evaluating the contribution of current citizen involvement initiatives and guide them in developing better strategies for leveraging the potential of these initiatives to benefit the circular economy and sustainable waste management.

Decomposition with the Additive Model with Exponential Trend Curve and Detection of Seasonal Effect in Descriptive Time Series

This article examines decomposition with the additive model. The procedure of decomposition has involved the four basic components and requires a method that can adequately estimate and investigate the trend parameters, seasonal indices and residual component of the series. This article also consider test for seasonality in the additive model with exponential trend curve. The test is applied to the row and overall sample variances of the Buys-Ballot table to detect the presence of seasonal indices in time series.

Block Wise Trend Analysis and Extreme Events in Raipur District, India

The study was conducted in four blocks viz. Abhanpur, Arang, Raipur, and Tilda of Raipur district, using IMD gridded rainfall and maximum (Tx) and minimum (Tn) temperature data from 1951 to 2019. Trend analysis was performed with three non-parametric methods (Mann Kendall, Spearman's Rho, and Innovative Trend Analysis (ITA)) and one parametric test (Linear Regression analysis) for annual and seasonal rainfall and temperature. Extreme weather events were studied using Weather Cock and RClimdex software, calculating sixteen climatic indices. The data was divided into 1951-1986 and 1987-2019. The results demonstrated a significant decreasing trend (p<0.01) in rainfall by 10 to 15%. Maximum temperature had a significant increasing trend and minimum temperature significantly decreased (p<0.01) as per ITA. Other methods indicated non-significant decreasing trends for minimum temperature. ITA proved more effective in trend detection. The value of all indices differed before and after 1986, indicating noticeable climate changes. Increasing Warmer days, colder nights, and an expanding diurnal temperature range were observed, accompanied by increased heat wave incidence. Moderate drought episodes and all severe drought occurrences increased after 1986. The values of maximum and minimum temperatures reached new peaks. These findings highlight significant climate changes in the study area, emphasizing the need for sustainable climate management and adaptation strategies.

The Different Types of Noise and How They Effect Data Analysis

AbstractRandom (noisy) processes can be characterized by the way consecutive data are correlated. The data can be uncorrelated (white noise), short‐range correlated (often called red noise), or long‐range correlated (sometimes called pink noise). Here we describe the properties and applications of these different kinds of noise. We discuss, how they influence (i) the diffusion process, (ii) the occurrence of rare extreme events and (iii) the detection of an external trend that is superimposed on the noise; (ii) and (iii) are particularly relevant in the context of detecting anthropogenic global warming by data analysis.

Observed multi-decadal trends in subsurface temperature adjacent to the East Australian Current

Abstract. Sea surface temperature observations have shown that western boundary currents, such as the East Australian Current (EAC), are warming faster than the global average. However, we know little about coastal temperature trends inshore of these rapidly warming regions, particularly below the surface. In addition to this, warming rates are typically estimated linearly, making it difficult to know how these rates have changed over time. Here we use long-term in situ temperature observations through the water column at five coastal sites between approximately 27.3–42.6∘ S to estimate warming trends between the ocean surface and the bottom. Using an advanced trend detection method, we find accelerating warming trends at multiple depths in the EAC extension region at 34.1 and 42.6∘ S. We see accelerating trends at the surface and bottom at 34.1∘ S but similar trends in the top 20 m at 42.6∘ S. We compare several methods, estimate uncertainty, and place our results in the context of previously reported trends, highlighting that magnitudes are depth-dependent, vary across latitude, and are sensitive to the data time period chosen. The spatial and temporal variability in the long-term temperature trends highlight the important role of regional dynamics against a background of broad-scale ocean warming. Moreover, considering that recent studies of ocean warming typically focus on surface data only, our results show the necessity of subsurface data for the improved understanding of regional climate change impacts.

Assessment of spatiotemporal variability of rainfall and surface wind speed over the eastern coastal agro-ecological zones of India using advanced trend detection approaches

Assessment of spatiotemporal variability of rainfall and surface wind speed over the eastern coastal agro-ecological zones of India using advanced trend detection approaches

An Interactive Web-Based Dashboard to Examine Trending Topics: Application to Financial Journals

Understanding trends is helpful to identify future behaviours in the field, and the roles of people, places, and institutions in setting those trends. Although traditional clustering strategies can group articles into topics, these techniques do not focus on topics over limited timescales; additionally, even when articles are grouped, the generated results are extensive and difficult to navigate. To address these concerns, we create an interactive dashboard that helps an expert in the field to better understand and quantify trends in their area of research. Trend detection is performed using the time-biased document clustering method. The developed and freely available web application enables users to detect well-defined trending topics by experimenting with various levels of temporal bias — from detecting short-timescale trends to allowing those trends to spread over longer times. Experts can readily drill down into the identified topics to understand their meaning through keywords, example articles, and time range. Overall, the interactive dashboard will allow experts in the field to sift through the vast literature to identify the concepts, people, places, and institutions most critical to the field.

Greening and Browning Trends on the Pacific Slope of Peru and Northern Chile

Accurate detection and quantification of regional vegetation trends are essential for understanding the dynamics of landscape ecology and vegetation distribution. We applied a comprehensive trend analysis to satellite data to describe geospatial changes in vegetation along the Pacific slope of Peru and northern Chile, from sea level to the continental divide, a region characterised by biologically unique and highly sensitive arid and semi-arid environments. Our statistical analyses show broad regional patterns of positive trends in EVI, called “greening”, alongside patterns of “browning”, where trends are negative between 2000 and 2020. The coastal plain and foothills, up 1000 m, contain notable greening of the coastal Lomas and newly irrigated agricultural lands occurring alongside browning trends related to changes in land use practices and urban development. Strikingly, the precordilleras show a distinct ‘greening strip’, which extends from approximately 6°S to 22°S, with an altitudinal trend, ascending from the tropical lowlands (170–780 m) in northern Peru to the subtropics (1000–2800 m) in central Peru and temperate zone (2600–4300 m) in southern Peru and northern Chile. We find that the geographical characteristics of the greening strip do not match climate zones previously established by Köppen and Geiger. Greening and browning trends in the coastal deserts and the high Andes lie within well defined climatic and life zones, producing variable but identifiable trends. However, the distinct Pacific slope greening presents an unexpected distribution with respect to the regional Köppen–Geiger climate and life zones. This work provides insights on understanding the effects of climate change on Peru’s diverse ecosystems in highly sensitive, biologically unique arid and semi-arid environments on the Pacific slope.

სამშენებლო კომპანიების ფინანსური მდგომარეობის ანალიზი და მისი სრულყოფის მიმართულებები

The best way to objectively evaluate the proper functioning and financial results of construction companies is to analyze their financial situation. As a result of the analysis of the actual situation, it is possible to make a forecast about the future stability of the company, or on the contrary, based on the mentioned information, expected threats can be avoided. The purpose of the present study is to develop directions for improving the financial condition of construction companies and to formulate appropriate conclusions and recommendations based on the practical analysis of the financial condition of these companies. There are a number of serious gaps in the regulations in the construction sector, which require timely completion. The analysis of the current processes in the construction sector reveals that the field is currently developing on its own. It is necessary to fully introduce modern construction regulations in Georgia and to regulate the construction field. It is important that continuous and active work in this direction is carried out. Regulation of the construction sector should be implemented through the coordinated work of state agencies, international organizations, construction industry and academia. It is important to fully realize the importance of international standards and make decisions with the participation of a properly informed construction sector. In our opinion, for the state regulation of business, it is necessary to correctly analyze the country's development indicators, take into account specific conditions and determine the possible directions of business development. A rational coordination of strategic and operational goals should be implemented and an organic relationship between the market and the state should be established, as a result of which the relations between the state and the construction market will be causally interconnected. Based on the analysis of the results of the research carried out by us, it is possible to formulate the main conclusions and recommendations of the existing problems identified in the research process: • Preparation and analysis of financial statements will provide significant assistance to the construction companies themselves (both management and owners) in objective assessment of past activities and making correct forecasts, therefore the financial services of construction companies should permanently analyze the main indicators of financial statements, study changes in these indicators and negative Taking preventive measures in case of detection of trends. • The ability to analyze financial statements will increase the interest of potential investors in terms of investing in a specific company, which in itself will help construction companies to find an alternative source of financing, that is why companies should provide presentation of financial statements in the language of business; • The availability of financial statements will allow stakeholders to calculate average risk-weighted figures for this particular area so that investors can conduct comparative analysis. Therefore, companies should ensure that audited financial statements are published publicly; • Preparation and submission of financial statements will promote business transparency, which is part of corporate responsibility to the people who buy construction products. Therefore, we consider it expedient to implement special trainings for raising the qualifications of financial managers and accountants of the construction sector, which will be drawn up directly taking into account the specifics of the field and will help the personnel working in this field to carry out reliable and legally compliant activities. According to the results of the research, we evaluated the solvency of "Anagi" LLC and "Ace Georgia" LLC and we can conclude that "Anagi" LLC is characterized by a steadily increasing development trend according to each of the indicators given above, while the financial indicators of "Ace Georgia" LLC do not allow making reliable forecasts. Since the company is characterized by a downward trend and its financial result for the considered three years is a loss. In the process of searching for the causes of these radically different results, we came to the conclusion that if the organization perfectly produces not only financial statements, but also management statements, its successful operation is not threatened. In this process, management reporting information is important especially because it allows the company's management to develop relevant financial policies to achieve the company's financial stability and profit maximization.

CONCEPT OF INFORMATION SYSTEM FOR CULTURAL HERITAGE SITES RENOVATION USING AUGMENTED REALITY

Cultural heritage is key to identity and development. Many valuable objects are affected by time, natural elements and financial lack. Innovative technologies are crucial for their preservation. The paper develops an information system based on augmented reality (AR) for the restoration of cultural heritage. This extends AURA's approach to preserving musical spaces by applying AR to cultural objects. New techniques improve AURA, allowing accurate restoration of objects affected by time. 3D modeling and machine learning allow to create virtual replicas with precision down to the smallest detail. Augmented reality and machine learning open new perspectives for the preservation of cultural values. The paper proposes an innovative approach of using AR for cultural heritage restoration. The authors offer unique solutions for accurate modeling of 3D models of objects. The purpose of the paper is to develop an information system for the restoration of cultural heritage through AR. This will increase the possibilities of preservation and research of values. Using AR and 3D modeling can improve the restoration of objects and provide access for researchers and the public. In future research, the proposed approaches and methods will be implemented to expand the functionality of the information system. This will include developing interactive interfaces for interacting with virtual models of cultural heritage, analyzing data for a deeper understanding of restoration processes and trend detection, as well as integrating cutting-edge information technologies, such as virtual reality and natural language recognition systems, using artificial intelligence. The primary goal is to improve the processes of preserving and studying cultural heritage through the use of modern information technologies.

Attribution of the heavy rainfall events leading to severe flooding in Western Europe during July 2021

In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging. However, for the larger Western European region it was found that, under current climate conditions, on average one rainfall event of this magnitude can be expected every 400 years at any given location. Consequently, within the entire region, events of similar magnitude are expected to occur more frequently than once in 400 years. Anthropogenic climate change has already increased the intensity of the maximum 1-day rainfall event in the summer season by 3–19 %. The likelihood of such an event to occur today compared to a 1.2 ^{circ }C cooler climate has increased by a factor of 1.2–9. Models indicate that intensity and frequency of such events will further increase with future global warming. While attribution of small-scale events remains challenging, this study shows that there is a robust increase in the likelihood and severity of rainfall events such as the ones causing extreme impacts in July 2021 when considering a larger region.

Detecting air pollutants trends using Mann-Kendall tests and Sen’s slope estimates

Recently, trend detection in ambient air pollutants has received a lot of interest, particularly in relation to climatic changes. Air pollutants data that were acquired from monitoring stations from 2015 to 2021 were used in the current investigation. The direction and size of the monotonic trend were determined using the Mann-Kendall test and Sen's slope estimator. The findings showed that there was significant fluctuation in different parameters over time. According to the study, SO2 and NO2 indicate a slightly increasing tendency with approximate annual concentrations of 6mg/m3 and 40mg/m3, respectively, whereas PM2.5 shows a decreasing trend with an approximate annual concentration of 130mg/m3. For all of Odisha's districts, PM10 exhibits no trend, with annual concentrations of about 90mg/m3. The study found that while NO2, PM2.5, and PM10 concentrations were significantly over the standard allowed limits while SO2 concentrations were significantly below them. Specific actions are needed to reduce these pollutants' emissions in Odisha.

Observations in the Spanish Mediterranean Waters: A Review and Update of Results of 30-Year Monitoring

The Instituto Español de Oceanografía (IEO, Spanish Institute of Oceanography) has maintained different monitoring programs in the Spanish Mediterranean waters (Western Mediterranean) since 1992. All these monitoring programs were unified in 2007 under the current program RADMED (series temporales de datos oceanográficos en el Mediterráneo), which is devoted to the in situ multidisciplinary sampling of the water column of coastal and open-sea waters by means of periodic oceanographic campaigns. These campaigns, together with a network of tide-gauges, are part of the IEO Observing system (IEOOS). In some cases, the temperature and salinity time series collected in the frame of these monitoring programs are now more than 30 years long, whereas sea level time series date to the beginning of the 1940s. This information has been complemented with international databases and has been analyzed in numerous works by the Grupo mediterráneo de Cambio Climático (GCC; Mediterranean Climate Change Group) for more than 20 years. These works have been devoted to the detection and quantification of the changes that climate change is producing on the physical, chemical, and biological properties of the Spanish Mediterranean waters. In this work, we review the results obtained by the GCC since 2005 in relation to the changes in the physical properties of the sea: water column temperature, salinity, and density, heat content, mixed layer depth, and sea level. Time series and results are updated from the last works, and the reliability of the existing time series for the detection of climatologies and long-term trends are analyzed. Furthermore, the different sources of uncertainty in the estimation of linear trends are considered in the present work. Besides this review and update of the results obtained from the data collected in the frame of the IEOOS, we conduct a review of the existing monitoring capabilities from other institutions in the Spanish Mediterranean waters and a review of results dealing with climate change in the Spanish Mediterranean obtained by such institutions. In particular, we include a review of the results obtained by SOCIB (Servicio de Observación y Predicción Costero de las Islas Baleares; Balearic Islands costal observing and forecasting system) in relation to the study of marine heat waves and the warming of the sea surface, and the results corresponding to the intense warming of the Catalan continental shelf at L’Estartit oceanographic station. All these results evidence that the surface Spanish Mediterranean waters are warming up at a rate higher than that affecting the global ocean (>2 °C/100 years). This warming and a salinity increase are also observed along the whole water column. Marine heat waves are increasing their intensity, frequency, and duration since 1982, and coastal sea level is increasing at a rate of 2.5 mm/yr. The salinity increase seems to have compensated for the warming, at least at surface and intermediate waters where no significant trends have been detected for the density. This could also be the reason for the lack of significant trends in the evolution of the mixed layer depth. All these results highlight the importance of monitoring the water column and the necessity of maintaining in situ sampling programs, which are essential for the study of changes that are occurring throughout the Spanish Mediterranean waters.