Long-term Trend Analysis Research Articles

Comprehensive analysis of long-term trends, meteorological influences, and ozone formation sensitivity in the Jakarta Greater Area

Jakarta Greater Area (JGA) has encountered recurrent challenges of air pollution, notably, high ozone levels. We investigate the trends of surface ozone (O3) changes from the air quality monitoring stations and resolve the contribution of meteorological drivers in urban Jakarta (2010–2019) and rural Bogor sites (2017–2019) using stepwise Multi Linear Regression. During 10 years of measurement, 41% of 1-h O3 concentrations exceeded Indonesia’ s national threshold in Jakarta. In Bogor, 0.1% surpassed the threshold during 3 years of available data records. The monthly average of maximum daily 8-h average (MDA8) O3 anomalies exhibited a downward trend at Jakarta sites while increasing at the rural site of Bogor. Meteorological and anthropogenic drivers contribute 30% and 70%, respectively, to the interannual O3 anomalies in Jakarta. Ozone formation sensitivity with satellite demonstrates that a slight decrease in NO2 and an increase in HCHO contributed to declining O3 in Jakarta with 10 years average of HCHO to NO2 ratio (FNR) of 3.7. Conversely, O3 increases in rural areas with a higher FNR of 4.4, likely due to the contribution from the natural emission of O3 precursors and the influence of meteorological factors that magnify the concentration.

A multivariate probabilistic framework for tracking the regional tropical edges: analysis of inter-annual variations and long-term trends

In the present study, a multivariate probabilistic framework is used to identify the meridional positions of regional tropical edges (RTEs), which are based on two variables: sea level pressure and precipitation minus evaporation. This new defined metric effectively captures inter-annual variability and long-term trend of the commonly adopted zonal mean tropical edge based on meridional mass stream function and near-surface winds. Besides, pronounced RTE trends are primarily located over the oceanic regions, and the terrestrial areas exhibit substantial inter-annual variability. These results are consistent among three modern reanalysis datasets. Moreover, the impacts of climate modes on RTE are investigated. The El Niño-Southern Oscillation, the Atlantic multi-decadal oscillation, and the Southern Annular Mode are important both on the inter-annual variations and long-term trends of RTE. The Pacific Decadal Oscillation is more inclined to affect long-term contribution rather than inter-annual relationship, and the Pacific–North American teleconnection, the North Atlantic Oscillation, and the Arctic oscillation highlight the inter-annual relationship with RTE in the specific regions, such as North Pacific, North Atlantic, and North Africa, respectively.

Groundwater Resource Assessment by Applying Long-Term Trend Analysis of Spring Discharge, Water Level, and Hydroclimatic Parameters

Groundwater Resource Assessment by Applying Long-Term Trend Analysis of Spring Discharge, Water Level, and Hydroclimatic Parameters

Trends in breast, colon, pancreatic, and uterine cancers in women during the COVID-19 pandemic in North Carolina.

The COVID-19 pandemic led to reductions in primary care and cancer screening visits, which may delay detection of some cancers. The impact on incidence has not been fully quantified. We examined change in cancer incidence to determine how the COVID-19 pandemic may have altered the characteristics of cancers diagnosed among women. This study included female patients aged ≥18 years and diagnosed with breast (n = 9489), colon (n = 958), pancreatic (n = 669), or uterine (n = 1991) cancer at three hospitals in North Carolina. Using interrupted time series, we compared incidence of cancers diagnosed between March 2020 and November 2020 (during pandemic) with cancers diagnosed between January 2016 and February 2020 (pre-pandemic). During the pandemic, incidence of breast and uterine cancers was significantly lower than expected compared to pre-pandemic (breast-18%, p = 0.03; uterine -20%, p = 0.05). Proportions of advanced pathologic stage and hormone receptor-negative breast cancers, and advanced clinical stage and large size uterine cancers were more prevalent during the pandemic. No significant changes in incidence were detected for pancreatic (-20%, p = 0.08) or colon (+14%, p = 0.30) cancers. In women, the COVID-19 pandemic resulted in a significant reduction in the incidence of breast and uterine cancers, but not colon or pancreatic cancers. A change in the proportion of poor prognosis breast and uterine cancers suggests that some cancers that otherwise would have been diagnosed at an earlier stage will be detected in later years. Continued analysis of long-term trends is needed to understand the full impact of the pandemic on cancer incidence and outcomes.

Prawo Unii Europejskiej a krajowe systemy prawne – przyczyny komplikacji

The aim of the article is to analyse the implementation of European Union law into the Polish legal system and the resulting expansion of legal acts at the national level.In Poland, the introduction of subsequent changes in regulations or the creation of new ones is justified by the need to adapt our law to the requirements of EU legislation.The research results presented in this article are part of a broader research project aimed at the analysis of long-term trends in Polish legislative policy. The project was conducted at Lazarski University by a team of researchers from different academic centres, with legal and economic background. In the process of data collection, an innovative research method was used based on the assumption that lawmaking is subject to the same long-term tendencies as any other commodity, i.e. the law of supply and demand. For the purpose of the study, 10 research questions on legislative trends were formulated. The study itself was a two-stage process: in the first stage, the researchers collected data; in the second, they analysed the data.In the publication, the authors presented the general results of the research, and then analysed and evaluated those laws in which the studied phenomenon of system complication occurred, understood as the introduction to the legal system of more complex and complicated regulations concerning the model of activities, in relation to those previously in force.

Analysis of long-term trends of rainfall and extreme rainfall events over Andaman & Nicobar and Lakshadweep Islands of India

The two major archipelagos of India, the Andaman & Nicobar Islands and the Lakshadweep situated in the climate-hazardous areas of the Bay of Bengal and Arabian Sea respectively are largely affected by weather systems developing over the sea and heavy rainfall activities. The recent two daily gridded rainfall data sets published by IMD; Rajeevan et al. (2010) at 1° × 1° spatial resolution and Pai et al. (2014) at 0.25° × 0.25° spatial resolution extending for a period of more than 100 years have been extensively used by researchers to study the rainfall characteristics at various spatiotemporal scales over the Indian mainland. However, these data sets do not include the grids over these two island meteorological subdivisions of India mainly because of the absence of daily rainfall observation for this long period. In this study, an attempt has been made to develop daily gridded rainfall data over these island subdivisions for the recent 70 years (1951 to 2020) in two spatial resolutions, viz., 1° × 1° and 0.25° × 0.25° using all the available islands station data during the period and carry out statistical analyses of various rainfall characteristics over these islands. The 0.25° × 0.25° data set was observed to be more comparable with the official rainfall time series of IMD for both these two Island subdivisions, and hence this data set has been used to carry out the trend analysis of Daily events of rainfall DER (> = 5 mm) for these two island subdivisions for the whole data period of 1951-2020 and the climate regime shift period of 1971-2020. DER was classified into two categories DMR (5-100 mm), daily moderate rainfall events and DHR (100 mm and above) daily heavy rainfall events. Signs and magnitude of the long-term trends in the frequency of DER (with DMR & DHR) showed significant changes during the recent period 1971-2020.

Long-term series wetland classification of Guangdong-Hong Kong-Macao Greater Bay Area based on APSMnet

Wetlands play a crucial role in achieving carbon peak and carbon neutrality goals. Exploring spatiotemporal distribution is one of the fundamental task in wetland research. However, existing large-scale and long-term series wetland mapping methods have challenges related to classification accuracy and obtaining inter-annual wetland samples. Therefore, a rapid sample collection and precise classification method are needed to support wetland resource assessment, conservation, and ecological restoration. In this paper, we propose a novel deep learning method suitable for large-scale and long-term series wetland classification. First, we acquire and preprocess the long-term series remote sensing data on the Google Earth Engine (GEE) platform. Second, the maximum wetland extent is extracted using decision trees and the Otsu algorithm (OTSU). Third, the attention and pyramid structure-based multidimensional feature extraction network (APSMnet) and the inter-annual sample migration algorithm (ISM) are proposed for realizing inter-annual wetland classification and wetland change analysis. Experiments conducted on Landsat 7 data of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2022 demonstrate that the overall accuracy (OA), average accuracy (AA), and kappa coefficient (Kappa) achieved by the proposed wetland classification method are 0.9664, 0.9396, and 0.9570, respectively. Long-term series trend analysis reveals that mangrove/swamp areas experience fluctuations influenced by urbanization and ecological policies, ultimately undergoing restoration and becoming stable. The codes and datasets are made available publicly at https://github.com/louanjun/APSMnet.

Direct and indirect impacts of land use/cover change on urban heat environment: a 15-year panel data study across 365 Chinese cities during summer daytime and nighttime

ContextLand use/cover change (LUCC) can directly and indirectly affect surface urban heat island intensity (SUHII) and the effects need to be decomposed.ObjectivesTo perform long-term trend analyses of contribution indexes (CIs) of land use types to urban heat environment in cities and to deconstruct direct and indirect effects of LUCC on SUHII within geographical regions.MethodsMann–Kendall test and Sen’s slope were used to examine the trends of CIs and SUHII in 365 cities during summer of 2005–2019. Structural equation models were established to quantify direct and indirect effects of land use types’ CIs on SUHII in six geographical regions of China.ResultsFirst, SUHII in 78.08% and 73.70% of the Chinese cities increased during summer daytime and nighttime, respectively. Second, the CI of built-up land significantly increased across more than half of the cities in all the six regions. Third, not all land use types exerted both direct and indirect effects on SUHII. At daytime, the CI of cropland (direct) was the dominant factor in East China (1.386), South-central (− 0.637), and Northwest (− 0.399) regions. At nighttime, the CI of water bodies (both direct and indirect) was the dominant factor in Northwest (0.506) and Northeast (0.697) regions while CI of built-up land (both direct and indirect) determined in North China (0.476).ConclusionsSeparation of direct and indirect effects of land use types on SUHII had practical implications for cities to optimize the structures and functions of ecosystems and to take regionally based actions improving the urban heat environment.

Trend analysis and change-point detection of monsoon rainfall in Uttarakhand and its impact on vegetation productivity

This study analyzes the long-term spatio-temporal changes and trend analysis in rainfall using the data from 1901 to 2020 and its impact on vegetation from 2000 to 2020 across districts of Uttarakhand. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test was performed to analyze the rainfall trend. Results show that the most of the districts exhibited significant negative trend of rainfall in monsoon, except two districts. Out of 13 districts, 4 districts recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for 7 districts of Uttarakhand. Furthermore, the significant negative trend (-2.23) was recorded for overall monsoon rainfall of Uttarakhand. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the districts of Uttarakhand. A significant decline rainfall was detected after 1960 while after 1970 interannual variability of rainfall was recorded to be increased. The analysis of month wise cumulative gross primary productivity (GPP) for 13 districts with rainfall trends shows that there is significant impact of rainfall trend on GPP during month of June and it gradually reduces for subsequent monsoon months. It was observed that the GPP of region is increasing at rate of 9.1 gCm-2d-1 in the region since 2000. Based on sensitivity analysis, the GPP of cropped area of region is more sensitive towards rainfall than forest area of Uttarakhand.

Increasing trend in hypertension prevalence among Korean adolescents from 2007 to 2020

BackgroundThe purpose of this study was to examine the prevalence of hypertension in Korean adolescents, its long-term trends, and factors associated with the development of hypertension.MethodsData of the Korea National Health and Nutrition Examination Survey (KNHANES) from 2007 to 2020 were combined into three time periods (2007–2011, 2012–2016, and 2017–2020). A total of 11,146 Korean adolescents aged 10–18 were included in the analysis. The definition of hypertension was based on the 2017 American Academy of Pediatrics guidelines for hypertension.ResultsThe age-adjusted prevalence of hypertension was 5.47%, 7.85%, and 9.92% in 2007–2011, 2012–2016, and 2017–2020, respectively. Long-term trend analysis using Joinpoint analysis over the observation period showed a significantly increasing trend in hypertension prevalence with a mean annual percentage change of 6.4%. Boys, those aged 13–15, those aged 16–18, overweight/obese, and those living in urban areas were more likely to develop hypertension (OR 1.980, 1.492, 3.180, 2.943, and 1.330, respectively).ConclusionThe prevalence of hypertension in Korean adolescents was higher than the global prevalence of hypertension and showed an increase over a 13–year period. Targeted strategies for prevention and early detection of hypertension are needed in this population.

Long-Term Time Series Trend Analysis of Climatic Variable for Jaisalmer District in Thar Desert, Western Rajasthan, India, Using Statistical Approaches

Long-Term Time Series Trend Analysis of Climatic Variable for Jaisalmer District in Thar Desert, Western Rajasthan, India, Using Statistical Approaches

Transitioning from MODIS to VIIRS Global Water Reservoir Product

Reservoirs play a crucial role in regulating water availability and enhancing water security. Here, we develop NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) based Global Water Reservoir (GWR) product, consisting of measurements of reservoir area, elevation, storage, evaporation rate, and evaporation loss for 164 large global reservoirs. The dataset is available at 8-day and monthly temporal resolutions. Since the Moderate Resolution Imaging Spectroradiometer (MODIS) is close to the end of its life, we further evaluated the consistency between MODIS and VIIRS-based GWR to ensure continuity to the 20+ year MODIS GWR product. Independent assessment of VIIRS reservoir storage (8-day) retrievals against in-situ measurements shows an average of R2 = 0.84, RMSE = 0.47 km3, and NRMSE = 16.45%. The evaporation rate has an average of R2 = 0.56, RMSE = 1.32 mm/day, and NRMSE = 28.14%. Furthermore, results show good consistency (R2 ≥ 0.90) between the VIIRS and MODIS-based product components, confirming that long-term data continuity can be achieved. This dataset can provide valuable insights for long-term trend analysis, hydrological modeling, and understanding hydroclimatic extremes in the context of reservoirs.

Effect of long-term climate signatures on regional and local potato yield in Finland

This research study investigates the impact of air temperature and precipitation on annual potato crop yield in Finland region and a local area in northern Finland. For this, the annual crop yield data of regional and local case study area is processed using Z-score normalization technique. Classification of computed z-score values was carried out into different classes ranging between the most beneficial crop yield year and the most vulnerable crop yield year. Later, the detection of feasible potato cropping season at monthly and daily scale was carried out using different weather parameters. Further, long-term trend analysis using Mann-Kendall's approach at annual, seasonal, and monthly scales was carried out using 60 years of dataset of both case study areas. Then after, the comparative analysis of annual crop yield and the characteristics obtained within climate data using different statistical approaches at annual, seasonal, and monthly scale. Finally, multivariate analysis was carried out to find most influential climate variables obtained using different statistical signatures. The results shows that, over the 100 years of period the annual potato crop yield of regional case study area shows a rising trend with declining area under potato crop. Classification approach found 16 % annual yield scored lowest Z-score while, 18% the highest. Likewise, 20 years of data for the local case study area found that 25 % of the dataset scored the highest Z-score and 15 % of the dataset values scored the lowest. Long-term trend analysis of air temperature shows significant increasing trend annually, seasonally and for monthly during the months between July-September. Whereas, similar procedure with the precipitation data showed increasing trend only at annual scale with the precipitation data analysis. During the comparative analysis between the annual crop yield data based on z-score classification and different statistical results obtained from weather parameters such as variation of precipitation and temperature sum, long-term climate data maximum-minimum totals seasonally as well as monthly during crop growing season; noticeable anomalies were observed in the weather parameters in line with the crop yield years grouped into the lowest and the highest Z-scores. The Multivariate analysis emphasized the impact of air temperature over precipitation on potato crop yield. Present study provides quantitative assessment on usefulness of long-term climate signatures for region specific either event-based or crop-based modelling development efforts to provide efficient decision support for enhancing farm-level crop productivity and sustainability of local and regional agriculture.

Redefining comorbidity understanding in rheumatoid arthritis through novel approaches using real-world data

Rheumatoid arthritis (RA) is a prevalent chronic disease that is associated with numerous comorbidities. Accurate assessment of these coexisting conditions, as reported by clinicians, is critical for an improved understanding of the impact of the disease and patient care. This perspective aims to evaluate the utility of real-world data (RWD) for enhancing the understanding of comorbidities in RA and to assess its potential in reshaping clinical management. RWD approaches, specifically the use of structured databases or data extracted from electronic health records, offer promising alternatives to overcome the limitations of traditional methodologies. Structured databases provide a systematic approach to data analysis, utilizing diagnosis codes to study large patient cohorts, revealing the prevalence of conditions, and demonstrating the potential for long-term disease trend analysis. Meanwhile, natural language processing (NLP) and artificial intelligence (AI) image analysis can bridge the gap between structured and unstructured data, by extracting meaningful information from unstructured fields such as free text or imaging. NLP has proven effective in the identification of RA patients and research outcomes, while AI image analysis has enabled the discovery of hidden findings in cardiovascular assessments, establishing a basis for the assessment of comorbidities in RA. However, while the benefits of using RWD are substantial, challenges remain. Ensuring comprehensive data capture, managing missing data, and improving data detection are key areas requiring attention. The involvement of clinicians and researchers in rheumatology is crucial in unlocking the potential of RWD studies, offering the promise of significant improvements in disease characterization and patient health outcomes.

Age-Period-Cohort Analysis of Long-Term Trends in Ischemic Stroke Mortality in China Caused by Specific Risk Factors from 1990 to 2019

Objective: The objective of this study was to study the primary risk factors for the long-term trends of mortality rates in ischemic stroke (IS) in China. Methods: Using the Global Burden of Disease Study 2019 (GBD 2019) database, research was conducted on the 11 primary risk factors for the mortality rates of IS in China from 1990 to 2019. This study employed joinpoint regression software and the age-period-cohort method to evaluate the trends of mortality rates divided by age, period, and cohort over time. Results: From 1990 to 2019, the age-standardized mortality rate (ASMR) caused by a diet high in red meat and high body mass index in China showed an upward trend. ASMR increased first and then decreased due to smoking, diet high in sodium, particulate matter pollution, high fasting plasma glucose, and high systolic blood pressure. Low-density lipoprotein cholesterol (LDL-C), kidney dysfunction, low temperature, and lead exposure remained relatively stable during this period. In the 35–45 age group, the mortality rate of IS due to high LDL-C was up to about 60%, and smoking affected men more than women. Overall, high LDL-C, high systolic blood pressure, and particulate matter pollution were the most common risk factors in patients with IS. The risk of death rose with age. The period and cohort relative risks showed that metabolic risk factors had the greatest impact on the mortality of IS. Conclusion: Metabolic risk factors have become the primary risk factors for the ASMR of IS in China. Relevant authorities should pay attention to their long-term effects on IS. Effective public health policies and interventions should be implemented to reduce the burden of IS.

FORESIGHT AS A TOOL FOR RESPONSIBLE PUBLIC MANAGEMENT AND ADJUSTMENT OF INTERNATIONAL ECONOMIC POLICY IN THE FOCUS OF GLOBAL DEVELOPMENT PROBLEMS

The purpose of the article is to study strategic long-term planning tools that allow timely identification of the latest risks, challenges and threats to international economic development that make it difficult to solve global problems that are a consequence of failures in the coordination of international economic policy. It has been determined that strategic forecasting allows politicians to increase the efficiency of government decision-making by identifying opportunities and threats that may arise in the coming years and decades, and also involves the development of possible strategies to counter them by taking into account the characteristics of the identified trends. The phenomenon of the new economy is characterized. The influence of technological development on the asymmetry of international economic development is determined. It has been established that the problems of international economic development in foresight strategizing include geopolitical rivalry, resource confrontation, energy dependence, deglobalization, disintegration processes, trade fragmentation, gaps in value and supply chains, reinstitutionalization of the system of international economic relations, recession, population aging, unemployment. Foresight forecasting tools that are not aimed at predicting a single “correct future” include: analysis of long-term trends and uncertainties; horizon scanning); scenario planning; shaping a normative future; political games; futures wheels; causal multilevel analysis. Research into long-term trends and uncertainties reveals key factors in different possible futures. The comprehensive observation carried out allows us to identify risks for the macroeconomic environment and proactively formulate a pool of measures to minimize the destructive impact of instability on socio-economic development. Improving foresight research as a critical element of strategic forecasting for solving problems of long-term development affects both theoretical aspects (conceptual approach, conceptual apparatus) and applied aspects (technologies and methods of development) of the development and use of this tool. Strategic planning allows you to develop adequate tools to achieve goals and proactively respond to the challenges of modern development, minimizing costs and compensating for the losses that arise for this reason, which lead to the loss of existing competitive advantages. The difference between forecasts and foresights is noted: forecasts are usually associated with uncontrollable events, while foresights most often deal with events and processes that are clearly controllable, therefore they assess the probability of not only one or another outcome of future events, but also the adoption of certain strategic and management decisions. The ability to distinguish normative approaches (what we want) from pragmatic approaches (what we should) is an important component of thinking in terms of alternative futures

Temporal phytoplankton dynamics and environmental variables in four Ethiopian soda lakes

We investigated the spatio-temporal dynamics of phytoplankton composition, chlorophyll-a as a proxy for algal biomass, and abundance in relation to environmental parameters in four Ethiopian soda lakes: Arenguade, Beseka, Chittu, and Shala. Triplicate water samples were collected from each lake from January to December 2020, four times in different seasons. Lake Chittu had the highest chlorophyll-a concentration, followed by Lake Arenguade, Beseka and Shala. Chlorophyll-a concentrations generally increased during the post rainy and dry season. The results of LR models are high for lakes Arengude, Beseka and Chittu. Lakes Shala and Beseka had the highest number of phytoplankton taxa, with both taxa composition and abundance dominated by Bacillariophyceae. Cyanoprokaryota, particularly Limnospira fusiformis, predominated in the abundance of Lakes Arenguade and Chittu. Water temperature, Secchi depth, turbidity, electrical conductivity, soluble reactive phosphorus, nitrate and silica significantly influenced the phytoplankton community structure. Long-term trend analysis revealed changes in phytoplankton biomass and lake taxonomic composition. The alteration in phytoplankton biomass and species composition of the lakes could be attributed to three factors: (1) frequent high-velocity explosions conducted for seismological studies in the past. This impact caused a dramatic increase in lake level in the case of Lake Beseka leading to a drop in nutrient concentration; (2) climate change and (3) salt content. Overall, our findings suggest that phytoplankton composition, biomass, and abundance varied according to seasonal fluctuations, emphasizing the possible effects of anthropogenic and natural causes on their community structure.

Analysis of temperature and precipitation series of Hirfanli Dam Basin by Mann Kendall, Spearman’s Rho and Innovative Trend Analysis

In this study long-term trend analysis of precipitation and temperature series are determined in the Hirfanli dam basin of Turkey. Data is obtained from the Turkish State Meteorological Service for the period of 1968 to 2017 for Gemerek, Kayseri, Kirsehir, Nevsehir, Sivas and Zara. Mann-Kendall, Spearman's Rho and Innovative Trend Analysis are used for trend analysis with 95% confidence levels. According to the results of the temperature series upward trend were determined. The results of all methods are similar but increasing significant trends were determined by Mann Kendall and Spearman's Rho except Zara. According to the precipitation series results, with decreasing trends in Gemerek, Kirsehir, Nevsehir and Zara, increasing trends were determined in Kayseri and Sivas. The results of Mann Kendall and Spearman's Rho methods show parallelism with each other. Contrary to other methods, Innovative Trend Analysis determined a decreasing trend in Kayseri. As a result of the analysis, the trends in the precipitation series are not significant at the 95% confidence level. In addition to statistical analyzes, evaluations were made in terms of integrated disaster management for drought disaster in the basin with arid climate characteristics.

Long-term spatial and temporal trend analysis of stream flow and suspended sediment transport of Godavari River Basin, India

Long-term spatial and temporal trend analysis of stream flow and suspended sediment transport of Godavari River Basin, India

Temporal Assessment of GCM-Driven Hydroclimatic Conditions for the Alberta Oil Sands Region, Canada

Abstract Freshwater supplies in most western Canadian watersheds are threatened by the warming of temperatures because it alters the snow-dominated hydrologic patterns that characterize these cold regions. In this study, we used datasets from 12 climate simulations, which are associated with seven global climate models and four future scenarios and are participating in phase 6 of the Coupled Model Intercomparison Project, to calculate and assess the historical and future temporal patterns of 13 hydroclimate indicators relevant to water resources management. We conducted linear long-term trend and change analyses on their annual time series to provide insight into the potential regional impacts of the detected changes on water availability for all users. We implemented our framework on the Alberta oil sands region in Canada to support the monitoring of environmental changes in this region, relative to the established baseline 1985–2014. Our analysis indicates a persistent increase in the occurrence of extreme hot temperatures, fewer extreme cold temperatures, and an increase in warm spells and heatwaves, while precipitation-related indices show minor changes. Consequently, deficits in regional water availability during summer and water-year periods, as depicted by the standardized precipitation evapotranspiration indices, are expected. The combined effects of the strong climate warming signals and the small increases in precipitation annual amounts generally detected in this study suggest that drier conditions may become severe and frequent in the Alberta oil sands region. The challenging climate change risks identified for this region should therefore be continuously monitored, updated, and integrated to support a sustainable management for all water users.