Water Temperature Time Series Research Articles

Analysis of irregularly sampled stream temperature time series: challenges and solutions

Water temperature is a key physical indicator of stream health, and as such, is commonly measured as part of long-term river health monitoring programs. However, analysis of long-term stream water temperature time series can be challenging, due to typically low frequencies of sampling combined with common characteristics of data collection from streams – such as observations unevenly spaced across seasons, changes to routine sampling frequencies, or improvements in the accuracy of measurements over time – known as sampling artifacts. While there are many models regularly used to estimate trends and summary statistics in long-term stream temperature datasets, there is limited understanding of the impact that commonly encountered sampling artifacts have on the accuracy and uncertainty of their estimates. This study constructed Monte-Carlo simulations to examine the influence that common sampling artifacts and the choice of analysis model can have on trend and mean estimates from long-term stream temperature time series covering tropical, temperate and cold climates. We found that, if not appropriately accounted for during analysis, sampling artifacts may obscure true trends or summary statistics, such as site means, and lead to inaccurate or misleading estimates. However, models that included components to account for seasonal variation within the model structure could estimate trends and means with high confidence, in the presence of almost all of the sampling artifacts commonly found in long-term stream temperature datasets. Structural biases in the time-of-day of sampling, such as always sampling in the morning, or where the start and end of the record are sampled at different times of day, could make estimates highly inaccurate and uncertain, and should be avoided in data collection strategies. This work aims to facilitate the analysis of historical stream temperature datasets with confidence, through identifying models that perform reliably in the presence of common sampling artifacts. The findings will enable further global insights into stream temperature, support management decisions based on accurate analysis and assist the design of future stream sampling programs using cost-effective, low-frequency sampling strategies.

Warming surface and Lake heatwaves as key drivers to harmful algal Blooms: A case study of Lake Dianchi, China

Extreme heat events such as marine and atmospheric heatwaves, have catastrophic impacts on marine and terrestrial ecosystems. Recent studies have shown that the continuous warming of lake surfaces and enhancement of lake heatwaves (LHWs) have generated great research interest globally. However, the effects of LHWs on lake ecosystems, especially cyanobacterial harmful blooms (cHABs), remain unclear. This study reconstructed the daily Lake surface water temperature (LSWT) time series in Lake Dianchi from 1951 to 2020 using random forest. Trends and characteristics of LSWT and LHWs were identified, and their critical driving effects on cHABs were analyzed. Results showed that the LSWT in Lake Dianchi significantly increased with a rate of 0.26 °C/10a and with an abrupt changing point in 1992, coinciding with the first report of severe cHABs in Lake Dianchi. From 1951 to 2020, 141 LHWs were detected, of which 94.3 % occurred after 1992. Among all the potential climatic and water quality driving factors on Chlorophyll-a (Chla) variation, LSWT is the most important. Chla concentration and the intensity of LHWs were significantly correlated. The complex and strong interactions between LHWs and cHABs enable the suppression of cHABs only when both remain low. A case study in Lake Dianchi highlighted the critical driving effects of LSWT and LHWs on cHABs, suggesting that continuously warming surface and enhancing LHWs in the future will further challenge more specific and effective lake management strategies to suppress cHABs.

Two-pathway spatiotemporal representation learning for extreme water temperature prediction

Accurate predictions of extreme water temperatures are criticalto understanding the variability of the marine environment and reducing marine disasters maximized by global warming. In this study, we propose a two-pathway framework with separated spatial and temporal encoders for accurate prediction of water temperature, especially extremely high water temperature, through effective spatiotemporal representation learning. The spatial and temporal encoder networks based on the Transformer’s self-attention mechanism performs the task of predicting the water temperature time series at the 16 coastal locations around the Korean Peninsula for the seven consecutive days ahead at daily intervals with various combinations of patch embedding methods, positional embedding for spatial features. Comparative experiments with conventional deep convolutional and recurrent networks are also conducted for comparison. By comparing and assessing these results, the proposed two-pathway framework can improve the predictability of extremely high coastal water temperature by better capturing spatiotemporal interrelationships and long-range dependencies from open ocean and regional sea, and further determines the optimal architectural details of self-attention-based spatial and temporal encoders. Furthermore, to examine the explainability of the proposed model and its consistency with domain knowledge, spatial and temporal attention maps are visualized and analyzed that represents weights for spatiotemporal input sequences that are more relevant to predict for future predictions.

Combining Landsat TIR‐imagery data and ERA5 reanalysis information with different calibration strategies to improve simulations of streamflow and river temperature in the Canadian Subarctic

AbstractArctic and Subarctic environments are among the most vulnerable regions to climate change. Increases in liquid precipitation and changes in snowmelt onset are cited as the main drivers of change in streamflow and water temperature patterns in some of the largest rivers of the Canadian Arctic. However, in spite of this evidence, there is still a lack of research on water temperature, particularly in the eastern Canadian Arctic. In this paper, we use the CEQUEAU hydrological‐water temperature model to derive consistent long‐term daily flow and stream temperature time series in Aux Mélèzes River, a non‐regulated basin (41 297 km2) in the eastern Canadian subarctic. The model was forced using reanalysis data from the fifth‐generation ECMWF atmospheric reanalyses (ERA5) from 1979 to 2020. We used water temperature derived from thermal infrared (TIR) images as reference data to calibrate CEQUEAU's water temperature model, with calibration performed using single‐site, multi‐site, and upscaling factors approaches. Our results indicate that the CEQUEAU model can simulate streamflow patterns in the river and shows excellent spatiotemporal performance with Kling‐Gupta Efficiency (KGE) metric >0.8. Using the best‐performing flow simulation as one of the inputs allowed us to produce synthetic daily water temperature time series throughout the basin, with the multi‐site calibration approach being the most accurate with root mean square errors (RMSE) <2.0°C. The validation of the water temperature simulations with a three‐year in situ data logger dataset yielded an RMSE = 1.38°C for the summer temperatures, highlighting the robustness of the calibrated parameters and the chosen calibration strategy. This research demonstrates the reliability of TIR imagery and ERA5 as sources of model calibration data in data‐sparse environments and underlines the CEQUEAU model as an assessment tool, opening the door to its use to assess climate change impact on the arctic regions of Canada.

A potential growth thermal index for estimating juvenile Atlantic salmon (Salmo salar) size-at-age across geographical scales.

We present a potential growth thermal index (PGTI) and assess its correlation with juvenile Atlantic salmon Salmo salar fork length data collected near the end of the growth season in a range of latitudinal locations and geographic scales (watershed, regional, continental) across the American north-east. The PGTI is based on two components: a water temperature-dependent growth curve and a water temperature time series continuously describing the thermal environment preceding fish sampling. Testing various shapes and characteristics of the temperature-growth curve against fish length data revealed strong positive correlations for all combinations. PGTI warming, calculated only from the beginning of the growth season until maximum summer temperature is reached, consistently performed well in explaining fish size-at-age across the latitudinal gradient and the three geographic scales that were considered. Varying thermal contrasts created by repeat subsampling of the dataset showed that fish length is better explained by the level of thermal contrast within the dataset than the geographical scale of analysis. A simple generalized linear model using a log link function with PGTI warming, fish density and water discharge as predictors explained 87% of the variance of size-at-age of 0+ and 1+ juvenile Atlantic salmon.

Bayesian analysis of high-frequency water temperature time series through Markov switching autoregressive models

Bayesian analysis of high-frequency water temperature time series through Markov switching autoregressive models

Coupling habitat-specific temperature scenarios with tolerance landscape to predict the impacts of climate change on farmed bivalves

Due to climate change, heatwaves are likely to become more frequent, prolonged and characterized by higher peak values, compared with climatological averages. However, the thermal tolerance of organisms depends on the actual exposure, which can be modulated by environmental context and microhabitat characteristics. This study investigated the frequency of occurrence of mass mortality events in the next decades for two species of farmed bivalves, the mussel Mytilus galloprovincialis and the clam Ruditapes philippinarum, in a shallow coastal lagoon, characterised by marked diurnal oscillations of water temperature. The effect of heatwaves was estimated by means of tolerance landscape models, which predict the occurrence of 50% mortality based on the exposure intensity and duration. Scenarios of water temperature up to the year 2100 were modelled by combining two mechanistic components, namely: 1) monthly mean water temperatures, simulated using a hydrodynamic model including the heat budget; 2) daily oscillations, estimated from the harmonic analysis of a twenty year-long site-specific time series of water temperature. Scenarios of mean daily sediment temperature were estimated by means of a cross-correlation model, using as input the water temperature one: the model parameters were estimated based on a comprehensive set of site-specific water and sediment temperature observations. The results indicate that for both species the risk of mass mortality rapidly increases starting from the 2060s. Furthermore, the daily patterns of water temperature seemed to be relevant, as overnight it falls below the predicted mortality thresholds for a few hours. These findings suggest that further studies should address: 1) the improvement of tolerance landscape models, in order to take into account the integrated effect of repeated non-lethal stress events on mortality rate; 2) the prediction of environmental temperature in specific habitat, by means of both process-based and data driven models.

Detection of global change with meiofauna and its benthic environment in a shelf sea cold water mass ecosystem

The marine benthic ecosystems can be affected by global environmental change. The northern Yellow Sea cold water mass (NYSCWM) is locally generated without external intrusion, and relatively independent and sensitive to global change. We utilized multiple scale temporal data collected during 12 cruises (2006–2014) to detect potential impact of global change on meiofauna and its benthic environment in the NYSCWM ecosystem. Time series of bottom water temperature and total organic carbon exhibited increasing trends, while those of meiofaunal abundance, biomass, sedimentary chlorophyll a and bottom water salinity exhibited decreasing trends. The temporal trends observed in our study were coincident with the global reduction of benthic standing stock in response to global change and anthropogenic disturbance. The strong seasonality of meiofauna, however, may mask its long-term pattern, and meiofauna with its sedimentary environment may serve as an integrated indicator of potential impact of global change on the shelf sea cold water mass ecosystem only if longer-term monitoring data can be accumulated and seasonal effect taken into account.

Water Cycles and Geothermal Processes in a Volcanic Crater Lake

Exploring how the hydrological and thermal conditions of a volcanic lake change in response to volcanic activity is important to identify the signs of a volcanic eruption. A water cycle system and a geothermal process in a crater lake, Okama, in the active Zao Volcano, Japan, were explored by estimating the hydrological and chemical budgets of the lake, and analyzing the time series of lake water temperature, respectively. In 2021, the lake level consistently increased by snowmelt plus rainfall in May–June, and then stayed nearly constant in the rainfall season of July–September. The hydrological budget estimated during the increasing lake level indicated that the net groundwater inflow is at any time positive. This suggests that the groundwater inflow to the lake is controlled by the water percolation into volcanic debris from the melting of snow that remained in the catchment. Solving the simultaneous equation from the hydrological and chemical budgets evaluated the groundwater inflow, Gin, at 0.012–0.040 m3/s, and the groundwater outflow, Gout, at 0.012–0.027 m3/s in May–September 2021. By adding the 2020 values of Gin and Gout evaluated at the relatively high lake level, it was found that Gin and Gout exhibit highly negative and positive correlations (R2 = 0.661 and 0.848; p < 0.01) with the lake level, respectively. In the completely ice-covered season of 15 December 2021–28 February 2022, the lake water temperature increased between the bottom and 15 m above the bottom at the deepest point, which reflects the geothermal heat input at the bottom. The heat storage change during the increasing water temperature was evaluated at a range of −0.4–5.5 W/m2 as the 10-day moving average heat flux. By accumulating the daily heat storage change for the calculated period, the water temperature averaged over the heated layer increased from 1.08 to 1.56 °C. The small temperature increase reflects a stagnant state of volcanic activity in the Zao Volcano. The present study could be useful to investigate how an active volcano responds to water percolation and geothermal heat.

Investigating Hydroclimatic Variables Trends on the Natural Lakes of Western Greece Using Earth Observation Data.

Expected global climate change is allegedly becoming more intense, and the impacts on water resources are being tracked in various hydroclimatic regimes. The present research investigates a hydrologically important area of Greece, where four natural lakes are concentrated. It aims to quantify any potential long-term trends in lake water area, precipitation, and temperature timeseries. Water area timeseries spanning four decades are estimated by the mNDWI from Landsat satellite imagery and used as an index of each lake's water storage. Precipitation and temperature measurements are obtained from the open access datasets Hydroscope and ERA5-Land, respectively. All of the timeseries were tested seasonally and annually with the Pettitt and Mann-Kendal tests for statistically significant breakpoints and trends detection. No timeseries analysis resulted in a statistically significant (at 0.05 or 0.1 levels) annual or seasonal trend. The hydroclimatic regime over the past forty years in western Greece is found to have been relatively stable. Land use was also assessed to have been relatively unchanging, converging to the overall stability of the local water regime. However, the findings of this research should not be interpreted as a reassurance against climate change, but as a call to further research for the detailed regional and local assessment of climate change and hydroclimatic variability with acknowledged statistical approaches.

Effects of atmospheric low-level jets on the mixing process of a large tropical reservoir.

Changes in the physical and biogeochemical properties of water columns are frequently associated with cold fronts and mesoscale convective systems due to increased cloud cover. The effects of low-level jet (LLJ) events on thermal stratification and water quality, however, remain undescribed, particularly for tropical reservoirs. Here, water temperature time series are combined with meteorological data, LIDAR observations, ERA5 reanalysis data, and hydrodynamical modeling to investigate the impact of an event of LLJ over the Furnas hydropower reservoir in Brazil. The LLJ event was characterized by dry, intense, and persistent winds (~10 m s-1) blowing for more than 12 hours over the main fetch of the reservoir. In the downwind side of the lake, the surface mixed layer depth increased by 50% during the LLJ event. The changes to the water column were produced by a combination of wind-induced upwelling, shear-driven mixing, and nocturnal convective overturning, different from the heat balance expected during passing cold fronts and mesoscale convective systems. The results suggest that both momentum and heat fluxes during LLJ events need to be accounted for in lake modelings to reproduce the vertical mixing process.

Data assimilation as a key step towards the implementation of an efficient management of dissolved oxygen in land-based aquaculture

A data assimilation (DA) methodology, e.g. the continuous-discrete Kalman filter (CD-KF), was applied to the assimilation of dissolved oxygen data, in order to obtain a dynamic estimation of the oxygen demand in a land-based aquafarm. The CD-KF was implemented on a dynamic model, which included as state variables the concentration of dissolved oxygen (DO) and fish respiration rate: the latter was considered as a non-observable stochastic variable. The model was applied to a 1-month long set of observations collected at a raceway rainbow trout farm, including (1) hourly time series of water temperature and dissolved oxygen concentration in the raceway influent and effluent and (2) a daily time series of fish number and fish weight distribution. The results show that the assimilation of DO data led to a dynamic estimate of DO demand which showed changes in the daily mean and the daily pattern: these were related to changes in the feeding regime. Furthermore, the methodology provided accurate short-term predictions of the DO concentration also in the presence of short-term fluctuations, which would be very difficult to relate to external forcings in a mechanistic model. These findings indicate that DA could be effectively used to design and implement efficient and robust control systems for optimizing the oxygen supply, thus contributing to the implementation of Precision Fish Farming in land-based aquafarms.

Neurohydrological prediction of water temperature and runoff time series

Neurohydrological prediction of water temperature and runoff time series

Keeping up with the math: Advancing the ecological foundation of the Great Lakes Cladophora Model

The nearshore waters of the Laurentian Great Lakes have historically suffered from beach fouling and clogged water intakes due to proliferation of the native, filamentous green alga Cladophora. A resurgence in nuisance growth of the alga has led to a demand for an improved model platform to better guide management. The Great Lakes Cladophora model (GLCM v3) predicts algal biomass (g dry matter m−2) and stored phosphorus content (P as % of dry matter) based on simulations forced by time series of incident light (I), water temperature (T) and water column soluble reactive phosphorus concentration (SRP, μgP L-1). A particular strength of the GLCM v3 is its foundation in ecologically sound biokinetic mechanisms, supported by field and laboratory measurements. These measurements, advancing the credibility and reliability of the biokinetic framework, include improved characterization of the growth and respiration responses to light and temperature, addition of a self-shading algorithm replacing an overly deterministic carrying capacity term, a new treatment of phosphorus uptake based on radioisotope experiments, additional observational support for Droop-based simulation of growth as a function of stored P, and implementation of a new physiologically and physically driven sloughing function. Uncertainty associated with processes collectively termed “environmental friction” (the I, T, and P growth forcing functions) is reduced, leaving the model sensitive to the maximum specific growth rate and the coefficient for extinction of photosynthetically active radiation through the algal mat. The model was performance tested by multi-lake (Erie, Huron, Ontario, and Michigan) calibration employing a common set of biophysical coefficients. This common set of calibration coefficients provides enhanced corroboration that GLCM v3 is suitable for examining the phosphorus–Cladophora dynamic across the Great Lakes. In particular, it greatly strengthens the model's efficacy for establishing a phosphorus standard to maintain levels of algal biomass below those constituting a nuisance condition, as per the Great Lakes Water Quality Agreement of 2012. In addition, the model structure can be applied to other lakes experiencing problems with attached filamentous algae.

Water temperature trends in the bottom layer of the Barents Sea in the summer season for the period 2004–2019

The aim: to determine the areas in the Barents Sea where the most significant changes in water temperature in the bottom layer occured over the last 15 years. Methods used: analysis of time series of water temperature in the bottom layer to identify trends in their structure, at grid nodes with steps of 10’ in latitude and 30’ in longitude; calculation of correlations with hydrometeorological parameters. Novelty: аreas with significant trends in water temperature in the bottom layer of the Barents Sea were identified on the basis of observations for 2004–2019, and trend coefficients were calculated. Result: for the investigated period, there were no significant changes in water temperature in the bottom layer in the central and southern parts of the Barents Sea. The area of positive trends was found off the western coast of the Novaya Zemlya Archipelago, with an increase in temperature by 0.1–0.2 °C per year. In the northern part of the sea, the area of decreasing trends in water temperature (by 0.1–0.2 °C per year) was identified. Practical significance: trend coefficients make it possible to estimate rates of changes in water temperature in the bottom layer, analyze features of year-to-year variability of water temperature, and consider the possible impact on ecosystem components.

Multi-scale variability of hydrothermal regime based on wavelet analysis - The middle reaches of the Yangtze River, China

Water temperature is a major driver of riverine ecosystems and has an extremely significant impact on them. Understanding the multi-scale water temperature dynamics in a river basin is critical to analyze the water temperature status of rivers. In this study, the intra-annual and inter-annual time series of water temperature (WT) at Yichang station in the middle reaches of the Yangtze River over the past 62 years was analyzed using complex Morlet wavelet functions to reveal the complex structure of water temperature variation at multiple time scales. The ecological impact of water temperature changes on the reproduction of the “Four Major Chinese Carp” under the influence of the Three Gorges Dam (TGD). The results showed that the water temperature at Yichang Station has a multi-level time scale structure, with an increasing trend at the inter-annual scale from 1956 to 2017, but different variations at the seasonal scale, and the water temperature cycles at both the inter-annual and seasonal scales have time scale variations of about 8–14 years and 4–7 years, with obvious characteristics of WT variation stages. The inter-annual and summer scales will have low WT in 2017–2022 and high WT in 2023–2027, while the other seasonal scales will have high WT in the next few years, either in the short or medium term. The correlation between air temperature and WT is the most significant among the three drivers of air temperature, flow and rainfall, and the correlation between WT and the air temperature is the most significant in winter scale under the influence of the Three Gorges Dam construction. Since the completion of TGD in 2003, the summer drainage temperature has decreased and the breeding period of the “Four Major Chinese Carp” has been shortened by 30–40 days compared to that before the construction of TGD. The results of this study can be used as a basis for further exploration of the formation mechanism of river water temperature and provide a scientific basis for river ecological protection.

Trends of lake temperature, mixing depth and ice cover thickness of European lakes during the last four decades

Lakes are particularly vulnerable ecosystems to global warming. Surface temperature of most lakes in the world has significantly increased. Here, we analysed time-series of water temperature, mixing-depth, and ice depth of 51 European lakes over the last four decades. We used data of surface temperature, total layer water temperature, mix-layer temperature, mix-layer depth, and ice cover depth obtained from the ERA5-Land reanalysis dataset. Our main objectives were a) to identify significant changes of the examined variables that have occurred from 1981 to 2019 and b) to assess the variability of changes in relation with geographical and lake morphological gradients. To this end, time series analysis was conducted using generalized additive models (GAMs). In addition, we quantified the magnitude of change by estimating the Sen's slopes for each variable and then we examined the variability of these slopes to geographical and lake morphological parameters using GAMs. Our results confirmed that water temperature parameters (surface, total-layer and mix-layer temperature) have significantly increased for all lakes during the last four decades. We also found significant changes of the mixing depth for 14 lakes. In addition, the lake ice depth has significantly decreased in all fifteen lakes of the subarctic climate region. Finally, we showed that the Sen's slopes depend on the geographic coordinates and the elevation of the lakes, whereas lake morphometry (e.g. depth) has a smaller effect on the magnitude of changes. These findings hint that lake ecosystems of Europe have substantially changed over the last forty years and urge the need to take precautionary measures to prevent future implications for the freshwater biota.

Incorporating temperature-dependent fish bioenergetics into a Narragansett Bay food web model

Food web models capture shifting species interactions, making them useful tools for exploring community responses to disturbances. The inclusion of environmental drivers, such as temperature, can improve model predictions, as energy demands of an organism can be temperature specific. While mass-balance models such as Ecopath with Ecosim (EwE) and the R implementation, Rpath, have included some thermal responses in past work, models have yet to include temperature-dependent energetic demands and metabolic costs. Our work demonstrates the inclusion of temperature-dependent bioenergetics into an Rpath food web model using the case study of a warming estuary: Narragansett Bay (Rhode Island, U.S.A). Thermal response parameters from literature were used to construct Kitchell curves describing temperature-dependent consumption and modified Arrhenius curves describing temperature-dependent respiration. Surface water temperature time series from 1994 to 2054 for high and low warming scenarios were created using observed temperatures and projections from the Coupled Model Intercomparison Project (CMIP6) multi-model ensemble. The integration of temperature-dependent fish bioenergetics resulted in lower projected biomasses as energetic demands increased. The degree to which biomass was impacted varied by functional group, though piscivorous fishes were particularly affected as both that group and their prey groups had forced bioenergetic changes. The differences in the model-predicted biomasses highlight the importance of accounting for thermal effects on marine species in ecosystem models, which will become increasingly important as ocean temperatures continue to rise in Narragansett Bay and elsewhere.

Adaptive smoothing to identify spatial structure in global lake ecological processes using satellite remote sensing data

Satellite remote sensing data are important to the study of environment problems at a global scale. The GloboLakes project aimed to use satellite remote sensing data to investigate the response of the major lakes on Earth to environmental conditions and change. The main challenge to statistical modelling is the identification of the spatial structure in global lake ecological processes from a large number of time series subject to incomplete data and varying uncertainty. This paper introduces a comprehensive modelling procedure, combining adaptive smoothing and functional data analysis, to estimate the smooth curves representing the trend and seasonal patterns in the time series and to cluster the curves over space. Two approaches, based on an irregular basis and an adaptive penalty matrix, are developed to account for the varying uncertainty induced by missing observations and specific constraints (e.g. substantive periods of measurement values of zero in winter). In particular, the adaptive penalty matrix applies a heavier penalty to smooth curve estimates where there is higher uncertainty to prevent over-fitting the noisy/biased data. The modelling procedure was applied to the lake surface water temperature (LSWT) time series from 732 largest lakes globally and the lake chlorophyll-a time series from 535 largest lakes globally. The procedure enabled the identification of nine global lake thermal regions based on the temporal dynamics of LSWT, and the extraction of eight global lake clusters based on the interannual variation in chlorophyll-a and ten clusters to differentiate the seasonal signals.

Warming of Atlantic Water in three west Spitsbergen fjords: recent patterns and century-long trends

We investigated the recent warming of summer Atlantic Water in relation to the century-long trends in maximum temperature in three west Spitsbergen fjords: Isfjorden, Grønfjorden and Billefjorden. On the basis of repeated along-fjord transects in late summer 2003–2019, we found that the warming has been pronounced not only in the outer but also in the inner domain of Isfjorden, where the presence of waters of Atlantic origin was registered more frequently after 2011 compared to early 2000s. Furthermore, Atlantic waters occurred more frequently in the bottom layers in the inner part of the fjord. In all the investigated fjords, the year 2014 was the warmest and saltiest during the period 2003–2019, which is consistent with previous reports for other west Spitsbergen fjords. In 2014, the mean temperature and salinity in Isfjorden and Grønfjorden exceeded 4.9 °C and 34.7 (in Billefjorden, 4.0 °C and 34.67, respectively). With the new data for 2010–19, we extended the time-series of maximum Atlantic Water temperature in Isfjorden and Grønfjorden, covering 1912–2009, reported previously by Pavlov et al. 2013. For the period 1912–2019, the average long-term trend of Atlantic Water maximum temperature is 0.25 °C/decade and 0.22 °C/decade in the outer part of Isfjorden and Grønfjorden, respectively. In the first two decades of the 21st century, the warming trend is steeper compared to the 20th century, 0.78 °C/decade in Isfjorden and 0.56 °C/decade in Grønfjorden, highlighting the strength of the ongoing ‘Atlantification’ of west Spitsbergen fjords.