This article presents the results of hydrological research on the Ruda River, which is the largest tributary of the Cetina River, located in the Dinaric karst of Croatia. The hydrology of this river has been altered after the construction of the Orlovac Hydropower Plant (HP) and the Buško Blato reservoir in 1973. The main aim of this study was to generate new knowledge about the hydrological functioning of the river, with a focus on the discharge and water temperature regimes that experienced the most severe alterations. The methodology is based on classical hydrological, statistical, and time-series analysis methods, adapted to the particularities of the study area and available data. Daily and hourly time series of air temperature, precipitation, water temperature, and discharge are analyzed to find trends, change points, inter-annual, seasonal, and sub-daily variations, durations, time shifts, and linear dependencies. The results obtained provide information on the effects of climate change, the duration of diffuse, conduit, and mixed flow, the importance of groundwater exchange, retention times, heat transfer times, and reference water temperatures. It determined the role of the operational mode of the Orlovac HP in discharge from the spring, in inter-annual and sub-annual water redistribution, and in hydropeaking and thermopeaking. The obtained information defines the present state of the Ruda River hydrology and illustrates alterations.

Abstract. The current status of intertidal waters in the wake of global change was assessed in a baseline study with a 36 month time series of water level, temperature, and salinity measurements from Bottsand lagoon, Baltic Sea, and from the mudflats off Schobüll, North Sea coast of Schleswig-Holstein, Germany. At Bottsand lagoon, annual average water temperatures varied from 12.1 to 12.6 °C, the air temperatures varied from 11.1 to 11.2 °C. The water temperatures followed the air temperatures in winter, and were higher than the air temperatures in spring and summer. The annual average salinities ranged from 14.7 to 16.9 units. The lagoon showed a different variability than the open Baltic surface waters, where the temperatures and salinities were lower in summer and higher in winter. The seasonal salinity differences were less developed in the mid 1960s, when the connectivity of the lagoon with the Baltic Sea was less restricted. In Husum Bight off Schobüll, annual average water temperatures ranged from 10.8 to 11.4 °C, and the air temperatures ranged from 9.9 to 10.2 °C. The water temperatures were lower than air temperatures in winter and higher in spring and summer, when the high waters were warmer during the day than at night-time. The annual average salinities off Schobüll ranged from 24.0 to 27.2 units. They were higher in summer and lower in winter, when the Elbe river runoff was generally enhanced. The same seasonal cycle was recorded in the Sylt Roads time series. Cross-correlations revealed that it takes seven weeks for an Elbe river freshwater pulse to reach Schobüll, and three weeks more to proceed to Sylt. The average salinities were 2.7 units lower off Schobüll than off Sylt. This pervasive gradient of landward decreasing salinities was induced by a local, low-salinity lens on top of tidal waters, fed by groundwater seepage or by freshwater runoff. A cross correlation with the precipitation record revealed salinity decreases about one week after high precipitation. The cumulative salt marsh submergence times per period of observation, i.e. inundation frequencies, were consistently higher at Bottsand than at Schobüll, where the same halophyte assemblages prevailed. As the average salinity was 10 units higher at Schobüll, the differences of inundation frequencies suggested that a certain salinity has to be maintained in the soils to sustain specific halophyte assemblages. A mass occurrence of small Austrominius modestus plates was observed before the vegetation boundary off Schobüll in spring 2024. The data suggested a wipe-out of juvenile barnacles during a short period of strong frost in late November 2023, when daily mean temperatures of up to −10.2 °C were recorded. This biotic response to environmental extremes highlighted the vulnerability of Wadden Sea ecosystems at times of Global Change.

In this study, we analyze water-temperature time series was measured over 34 years, between 1986 and 2020, at the water surface at seven stations across Lake Villarrica (Southern Chile). The spring and summer seasons show an increment in the superficial temperature during the study period. The annual maximum temperature, ranging between 17.35 and 21.65 °C were observed in 1997 and 2009, respectively, while the annual minimum, ranging between 16.8 and 21.5 °C were observed in 2001 and 2009, respectively. In addition, we employ a machine learning based estimation model to predict surface temperatures in a South American lake spanning the period 1989 to 2021. Our model uses data in situ of physical, chemical, and biological parameters of lake quality water, along with meteorological data and spectral bands, including combinations of images from the Landsat 8 satellite, as input variables. The 7 lake monitoring stations were classified into 4 regions according to their geographical location: north, south, east, and west. Our findings demonstrate the exceptional performance of the long short-term memory (LSTM) model in accurately estimating temperatures across Lake Villarrica. The best results were obtained for the west region of the lake with good statistical metrics from the estimation model of RMSE = 2.79, Bias =−0.06, max error = 5.93, MSE = 7.83 and median absolute error (MedAE) = 2.13. This approach represents a significant advance in the integration of remote sensing and machine learning techniques to monitor and manage inland water systems.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-23937-5.

ABSTRACT The measurement of the stream water temperature signal is subject to various issues and environmental phenomena. Accurate interpretations of the data composing water temperature time series (WTS) often require a high‐level human expertise during data preprocessing steps to sort out meaningful temperature signals. This study proposes a method to highlight two main types of particular behaviours encountered in WTS, apart from outliers: intensified data and buffered data. The method uses a metric based on the WTS itself to identify periods with particular data. It enables the identification and the visualisation of regular and irregular particular behaviours in a given WTS. The method was applied to a large national dataset collected in mainland France. The dataset contains 993 WTS with a wide range of data quality and environmental measurement conditions. Data identified as particular behaviour accounts for up to 7% of the dataset. Depending on the measurement conditions, up to 25% of a given WTS data can be considered as ‘occasional particular behaviour’ and potentially not exploitable. Buffered data mostly occur during winter months with no apparent spatial pattern. Intensified data occur mainly in summer months and a spatial pattern shows WTS containing the highest percentage of intensified data in the south‐east part of the study area. The identification method was also applied to several known situations where a high‐level human expertise was available. It provided robust identification performances at regional scale confronted with human expertise as well as at national scale, on a large dataset. Such methods can facilitate the selection of exploitable data in large datasets which are more widely available today. Potentially problematic data becomes straightforward and subsequent data qualification or correction is facilitated.

Time series of water temperature and conductivity obtained over three years of continuous measurements at seven autonomous moored stations north of the Severnaya Zemlya archipelago located in the Arctic Basin of the Arctic Ocean were analyzed in combination with numerical modeling to investigate the spatiotemporal variability of temperature and salinity in the intermediate layer of Atlantic-origin waters. These waters propagate along the Eurasian continental slope within the Arctic Boundary Current (ABC). Within 85 km of the shelf edge, three distinct branches of Atlantic Water (AW) transport were identified, each characterized by a unique origin history of origin that shapes the variability of its thermohaline properties. The most energetic mode of temporal variability at all stations is associated with oscillations with a period of approximately 12 months. The amplitude of these oscillations decreases with increasing distance from the shelf edge, while their phase differs among the AW branches. Numerical modeling indicates that, in the study region, the typical phase–distance relationship observed in the western Nansen Basin is disrupted by the large-scale input of cold, freshened water through the St. Anna Trough.

Stream metabolism is traditionally defined as the combined metabolism of all aerobic organisms in a stream. Its component processes of oxygenic photosynthesis and aerobic respiration create and consume dissolved oxygen (DO) and therefore can be measured using time series of DO concentration, solar radiation, and water temperature, in conjunction with a model of DO dynamics that includes photosynthesis, respiration, and oxygen exchange with the atmosphere. A complication is that stream communities typically exhibit pronounced longitudinal heterogeneity in habitat type (e.g., shaded versus unshaded reaches) and species composition and abundance. The influence of a given stream reach and associated community on DO concentration propagates downstream with the current, gradually being replaced, over a transition zone, by the influence of the next downstream reach. Knowing the approximate length of this transition zone is important when estimating stream metabolism with one-station DO monitoring, since it indicates which stream reach and associated community the metabolism estimates apply to. We propose new methods for estimating the transition-zone length and for estimating the proportions of DO at a given location in a stream reach that entered the reach from upstream, from photosynthesis within the reach, and from atmospheric uptake within the reach. We also propose methods for estimating the residence-time distribution of DO present at a given stream location, and the corresponding distribution of upstream distances at which the DO entered the stream. Both distributions are approximately exponential. Thus, habitat immediately upstream of the sonde has the greatest influence on metabolism estimates with one-station monitoring, and it is therefore important to place the sonde so this habitat is representative of the study reach.

Although lakes represent only 0.26% of the total freshwater on Earth, they provide essential environmental services, such as public water supply and irrigation. Water temperature, which drives the lake’s physical, chemical, and biological processes, is a parameter typically used for lake management plans. To explore the effects of climate change on Lake Mangueira, a subtropical coastal shallow lake, we assessed the cumulative intensity index, which links heatwave duration to mean intensity. The Air2Water model was calibrated using time series of observed daily air temperature data and water surface temperature data obtained via remote sensing. Based on climate projections obtained from 26 global climate models, the Air2Water model was used to generate water temperature time series for a historical period and future scenarios, including SSP1-2.6, SSP2-4.5, and SSP5-8.5. We found that at least 75% of the projections indicated lake heatwaves with a cumulative intensity exceeding 50 °C days under the SSP5-8.5 scenario, compared to 18 °C days in the historical period. Even in the least severe future scenario (SSP1-2.6), 75% of the projected average cumulative intensities were equal to or exceeded every value from the historical period. These findings highlight a concerning shift in the thermal dynamics of Lake Mangueira. The lake is expected to experience more intense and/or longer-lasting heatwaves that have the potential to significantly affect its aquatic communities. The cumulative intensity index can therefore be used to monitor extreme events in these ecosystems, which will help to properly manage them.

Stream metabolism is traditionally defined as the combined metabolism of all aerobic organisms in a stream. Its component processes of oxygenic photosynthesis and aerobic respiration create and consume dissolved oxygen (DO) and therefore can be measured using time series of DO concentration, solar radiation, and water temperature, in conjunction with a model of DO dynamics that includes photosynthesis, respiration, and oxygen exchange with the atmosphere. A complication is that stream communities typically exhibit pronounced longitudinal heterogeneity in habitat type (e.g., shaded versus unshaded reaches) and species composition and abundance. The influence of a given stream reach and associated community on DO concentration propagates downstream with the current, gradually being replaced, over a transition zone, by the influence of the next downstream reach. Knowing the approximate length of this transition zone is important when measuring stream metabolism based on DO dynamics and in designing stream restoration projects to improve DO and temperature levels for fish. We propose new methods for estimating the transition zone length and for estimating the proportions of DO at a given location in a stream reach that entered the reach from upstream, from photosynthesis within the reach, and from atmospheric uptake within the reach. We also propose methods for estimating the residence-time distribution of DO present at a given stream location, and the corresponding distribution of upstream distances at which the DO entered the stream.

Thermal physiology is a pivotal biotic factor for the ecophysiology of commercially valuable tuna, influencing not only horizontal but also vertical behaviors. We aimed to examine how the thermal physiology of skipjack tuna (Katsuwonus pelamis, SKJ) can explain the differences in their vertical behavior, focusing on surfacing and diving, among various thermal environments during their northward migration in the western North Pacific. We analyzed archival tag data collected during 2012-2015, with individual time series (Fork length: 38-49cm, N = 38) of swimming depth, water temperature, and peritoneal body temperature during northward migration from subtropical areas to temperate regions around Japan. We quantified surfacing and diving behavior as an index of vertical behavior and estimated the whole-body heat transfer coefficient (λ) during the cooling and warming phases associated with diving using body and water temperature records as indicators of thermal physiology. In the southern mixed layer areas, SKJ were widely distributed at a depth layer <200m, whereas they were restricted to the surface in the strong thermocline areas in the north. The dive duration was significantly shortened with a strong thermal gradient during northward migration. We observed minor to no differences in λ values between the cooling and warming phases in the southern areas, whereas the λ values in temperate areas differed by a factor of 2-3 between the phases. Our findings of changes in λ values between the cooling and warming phases represent the first evidence of thermoregulation in SKJ. Surfacing preference behavior and short dive duration in temperate areas may be an avoidance of prolonged exposure to cold temperatures, a behavior commonly exhibited in other tuna. Moreover, we discussed how the changes in vertical behavior driven by thermal physiology can explain spatial heterogeneity in SKJ fishery grounds in the western Pacific Ocean.

Climate-induced changes in river conditions, such as water temperature and current velocity, can disrupt species migration patterns by altering the conditions encountered during migration. We assessed these impacts across the Loire River basin in Europe by compiling long-term fish passage data for three diadromous fish species at fishways, alongside reconstructed time series of water temperature and current velocity. We considered both water temperature and current velocity on days when migration was observed as “suitable conditions” for the species’ upstream migration. Our findings for the 1963–2019 period revealed that trends in the frequency of suitable conditions for upstream migration were species-specific. For Atlantic salmon, particularly fall migrants, the frequency of suitable conditions has declined over time in certain areas. In contrast, the frequency of suitable migration conditions for both allis shad and sea lamprey has shown an overall increase across the basin. This study offers policy-makers and environmental managers valuable insights for developing effective, spatially explicit strategies to preserve diadromous fish populations amid ongoing climate change.

ABSTRACTThere is relatively little research on infiltration into seasonally frozen soils on mountain hillslopes and few evaluations of infiltration model performance in this environment exist. As a result, the application of existing infiltration estimation methods developed in level environments is uncertain for estimating spring runoff in mountain basins. A field study was conducted in the Canadian Rockies using 8 years of snowpack, liquid soil moisture, and temperature profile observations from steep north‐facing and south‐facing slopes. Seasonal infiltration was calculated using soil freezing characteristic curves, timeseries of soil volumetric water content and temperature. Infiltration was found to primarily follow the limited case postulated by Popov (1972), with only 1 year at one site undergoing unlimited infiltration where nearly all meltwater infiltrated. Infiltration was estimated using an equation for the limited case developed from extensive observations of seasonal infiltration, initial soil saturation, and peak SWE in Canadian prairie agricultural fields. Whilst this equation accurately estimated infiltration depths on these mountain hillslope sites, it was unsuitable for application due to a statistical association between its driving variables. Initial soil saturation had no influence on infiltration depths at these sites and so a simpler single‐variable infiltration equation to estimate infiltration depths based on peak SWE was developed and found to have good predictive capability. Alternative approaches using modelled cumulative melt or infiltration opportunity time also had good predictability. Runoff depths estimated from a water balance, assuming negligible evaporation and sub‐surface drainage, were reliably predicted using peak SWE or cumulative melt depths by single‐variable infiltration equations in the absence of soil moisture, texture, aspect, or slope information. The results provide insights into estimating snowmelt runoff on hillslopes from snowpack accumulation that has been observed in cold region mountains, despite the complexity of hillslope hydrology and frozen soil infiltration processes.

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

Two-pathway spatiotemporal representation learning for extreme water temperature prediction

Abstract Arctic 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 &gt;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) &lt;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.

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

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

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 &lt; 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.