Daily Air Temperature Research Articles

Unprecedented Human‐Perceived Heat Stress in 2021 Summer Over Western North America: Increasing Intensity and Frequency in a Warming Climate

AbstractThe unprecedented 2021 June‐July heatwave in Western North America resulted in record‐breaking human‐perceived heat stress across the region, measured by the humidex considering both air temperature and humidity. During extended summer (June‐September), both 95th percentiles of daily maximum humidex (HX95) and air temperature (TX95) have increased over the 1940–2022 period, with even faster intensification in the last two decades (2001–2022). HX95 has increased more than TX95 because of the positive monotonic nonlinear relationship between humidex and air temperature at a given level of relative humidity. The Canadian Earth System Model version 5 (CanESM5) projects a larger increase in human‐perceived heat stress than air temperature across the region under low to high emission scenarios (HX95 increases 4.40–7.04°C and TX95 increases 2.92–4.65°C between 1981–2010 and 2041–2060). Moreover, CanESM5 projects significant increases in the frequency of HX and TX conditions that exceed the levels reached in 2021 under intermediate and high emission scenarios.

Air temperature changes due to altitude above sea level in the Northern Ural Mountains

The research is devoted to an urgent modern problem: the identification of temperature factors that limit the distribution and survival of plants in the mountainous conditions of the Northern Urals. The article’s aim is to determine the air temperature in four altitudinal zones of the southern part of the Northern Urals (Sverdlovsk region 59º30´N, 59º15´E) and to identify regression relationships of the obtained temperature data with control temperature data from the nearest meteorological station. Registration of air temperatures was carried out from May to September 2019 around the clock, every two hours in the mountain forest zone (at an altitude of 460 and 640 m above sea level) under the canopy of Siberian stone pine forests, in the zone of subalpine woodlands with elements of mountain forest tundra (820 m above sea level) and on a plateau in the mountain tundra zone (1030 m above sea level). It has been established that the change in air temperature at different altitude levels and at the nearest meteorological station (far from 60 km, at an altitude of 202 m above sea level) occurs relatively synchronously. Difference between average daily temperatures at altitudes of 460, 640, 820 and 1030 m above sea level and the control data of the meteorological station is 2.2, 3.0, 4.7 and 5.1ºC respectively. For all altitude levels, a reliable close straight-line relationship between average daily air temperatures and meteorological station data has been established. The altitudes of 460, 640, 820 and 1030 m above sea level correspond to the coefficients of determination ( R 2) equal to 0.96, 0.95, 0.92 and 0.88. The relationship of the minimum temperatures of the corresponding altitude levels with the control data is also quite high ( R 2 is not lower than 0.7). With the help of the identified relationships and the obtained regression equations, it is possible to retrospectively restore the dynamics of the thermal regime according to the meteorological station data for mountain habitats of different altitudes in the southern part of the Northern Urals over a long period. Including extreme critical temperatures, which act as factors limiting the resettlement and survival of plants and determine the ecosystem biodiversity.

Unveiling lake ice phenology in Central Asia under climate change with MODIS data and a two-step classification approach

Lakes in Central Asia, characterized largely by a semiarid climate and delicate ecosystems, are quite sensitive and vulnerable to climate change but remain less exploited, due mostly to a lack of in-situ measurements. Lake ice phenology is an essential climate variable identified by the Global Climate Observation System. To overcome the overestimation of clouds by the MODIS cloud mask, we developed and distributed a two-step Random Forest algorithm based on MODIS data on the Google Earth Engine. This algorithm first distinguishes cloudy pixels from non-cloudy pixels, and then ice pixels are discriminated from non-cloudy pixels using additional thermal infrared information. We validated the algorithm by implementing it over several boreal lakes with in-situ records in North America and compared our results with existing lake ice phenology datasets. We subsequently retrieved lake ice phenology for 20 large lakes (> 100 km2) in Central Asia from 2001 to 2021 and examined trends in lake ice phenology over this period. On average, lakes in Central Asia underwent later freezing at 0.13 d/yr, earlier thawing at 0.32 d/yr, and shorter ice duration at 0.45 d/yr, with over half of the lakes witnessing significant trends in at least one ice phenology indicator. To evaluate the impact of meteorological factors on lake ice phenology, we built a linear mixed model that predicts ice phenology using meteorological variables from ERA5. Daily mean air temperature dominates ice phenology, with mean air temperature during October to June in the following year explaining approximately 35% of the variance of the duration between freezing and thawing for each lake. Our study offers a comprehensive analysis of lake ice phenology in Central Asia under climate change.

Effects of climatic factors on carbohydrate composition of soybeans in Argentina

AbstractCarbohydrates are the second largest component of soybean seeds and include soluble sugars, a major source of energy. However, some oligosaccharides such as the raffinose family of oligosaccharides (RFOs) are considered antinutritional factors. In Argentina, the soybean crop area shows high climatic variation during the seed‐filling period (R5–R7), affecting seed composition. The objective of this work was to evaluate the effect of climatic traits on carbohydrate (CHO) seed composition of different cultivar types. Daily air temperature and solar radiation were measured during R5–R7 in two soybean varieties: (non‐transgenic) ALIM3.14 and a commercial. Precipitation and evapotranspiration during the crop season (R1–R7) were used to calculate a hydric balance proxy. ALIM 3.14 showed a better CHO profile than the commercial soybean. ALIM 3.14 had lower stachyose and total RFOs (25.1 and 28.9 g kg−1) than the commercial cultivar (37.3 and 42.3 g kg−1), respectively. Air temperature explained variations in CHO. A decrease in sucrose was observed with the increase in mean temperature (−2.65 g 100 g−1 per °C). Sucrose/total RFO and total sugar concentration also declined with increasing mean temperature −0.10 g 100 g−1 per °C and −0.35 g 100 g−1 per °C, respectively. Soybean with desired nutritional profile for specialties, such as high sucrose concentration and low raffinose, were obtained at cool temperatures (<19.4°C) during seed filling. Producers and breeders can use this information to identify climatic variable thresholds that maximize the concentration of sugars in seeds and select traits and environments useful to improve soybean under climate warming conditions.

Daily air temperature forecasting using LSTM-CNN and GRU-CNN models

Daily air temperature forecasting using LSTM-CNN and GRU-CNN models

Numerical analysis of extreme heat in Nagpur city using heat stress indices, all-cause mortality and local climate zone classification

Spatio-temporal investigation of heat-related impacts forms the basis for planning resources and formulating possible countermeasures. The present study aims to examine the effect of temperature extremes on all-cause mortality in Nagpur, a tropical Indian city and investigates (i) long-term (1969–2015) daily air temperature trend (ii) long-term (1985–2015) heat stress pattern using heat stress indices (iii) relationship between heat stress indices and all-cause mortality, and (iv) role of local climate zones to estimate intra-urban variation in mortality. The findings reveal a steady rise in maximum temperature, heat wave, severe heat wave, and hot nights. It was observed that during 1985–2015, heat index and humidex increased by 4 °C and 1 °C respectively. A cubic spline regression revealed a strong influence (R2 > 0.75) between heat stress indices and all-cause mortality. The results indicate that large low-rise and sparsely built areas (i.e., local climate zones 8, 9 and 93) present higher levels of heat stress indicating greater susceptibility to mortality. In contrast, compact/open low-rise developments with vegetation cover showed lower susceptibility to heat stress. The findings provide potential evidence to develop hyperlocal thresholds, and strengthen early warning systems by selecting appropriate indices for a given climate zone.

ЭФФЕКТИВНОСТЬ МОДЕЛИРОВАНИЯ СТОКА РЕК БАССЕЙНА ВЕРХНЕЙ УССУРИ С ИСПОЛЬЗОВАНИЕМ ДАННЫХ НАБЛЮДЕНИЙ И РЕАНАЛИЗА ERA5

Experience of using meteorological observations and the ERA5 reanalysis for runoff modeling using the GR4J conceptual model is outlined. The study objects are catchments within the Ussuri River basin (Kirovskii, the Russian Far East). The results of the comparison of ground-based observations and reanalysis data are presented. The hydrological model has been calibrated and verified on the basis of various data sources. The traditional scores NSE, logNSE, and BIAS have been used to evaluate the modeling efficiency. According to the scores, the modeling efficiency is generally “satisfactory” and better. It is shown that for simulations, it is better to use observation network data in case of floods and the reanalysis data in case of spring high water and low flow periods. It is concluded that the effective resolution of the ERA5 data for daily precipitation and air temperature for hydrological modeling in the study area is 0.75°-1.0° (~90-120 km).

High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset

Gridded high-resolution climate datasets are increasingly important for a wide range of modelling applications. Here we present PISCOt (v1.2), a novel high spatial resolution (0.01°) dataset of daily air temperature for entire Peru (1981–2020). The dataset development involves four main steps: (i) quality control; (ii) gap-filling; (iii) homogenisation of weather stations, and (iv) spatial interpolation using additional data, a revised calculation sequence and an enhanced version control. This improved methodological framework enables capturing complex spatial variability of maximum and minimum air temperature at a more accurate scale compared to other existing datasets (e.g. PISCOt v1.1, ERA5-Land, TerraClimate, CHIRTS). PISCOt performs well with mean absolute errors of 1.4 °C and 1.2 °C for maximum and minimum air temperature, respectively. For the first time, PISCOt v1.2 adequately captures complex climatology at high spatiotemporal resolution and therefore provides a substantial improvement for numerous applications at local-regional level. This is particularly useful in view of data scarcity and urgently needed model-based decision making for climate change, water balance and ecosystem assessment studies in Peru.

Modern changes in climatic characteristics and minimum flow of the rivers in the basin of lake Sevan

Formulation of the problem. Studies and data on modern changes in the winter minimum average monthly flow of the rivers of the Lake Sevan basin and their climatic characteristics have acquired great practical importance and considerable interest due to the increased use of water resources for the purposes of various types of economic activities, in particular, in connection with the development of the economy, hydropower, irrigation, population growth and rising living standards. Especially relevant are the calculations of the minimum flow in relation to solving the problems of water supply for settlements, industrial enterprises. The aim of the work. Assessment of modern changes in the winter minimum average monthly runoff of rivers in the Lake Sevan basin and their climatic characteristics. Methods. The following methods were used in the work: mathematical-statistical, extrapolation, interpolation, spatial analysis, analogy, correlation and cartography. Results. A physical map has been compiled with a network of meteorological stations and hydrological posts in the Lake Sevan basin. Close correlations were obtained between the values of the winter minimum average monthly runoff and the average runoff of the winter period for a hydrological year. These dependences can be used for preliminary estimates of the minimum runoff of the winter low-water period of unstudied rivers in the territory under consideration. In the course of the research, the winter minimum average monthly discharges were calculated by months and for the entire observation period, the average value of the minimum runoff for the winter low water, and the linear trend of the climatic characteristic. Based on the obtained results, a map of the distribution of the modul of the average minimum runoff of the winter low-water period and the coefficients of the linear trend of the winter air temperature were constructed. Examples of curves of long-term changes in precipitation amounts for the autumn and winter periods, the sum of positive temperatures and the number of days with a positive average daily air temperature in the winter period for a series of meteorological stations in the study area are presented. The rivers of the Lake Sevan basin are characterized by an uneven spatio-temporal distribution of winter minimum runoff and climatic characteristics, changes in the winter average monthly minimum runoff, precipitation amounts for the autumn and winter periods are multidirectional, and air temperature changes are only an upward trend. In the area under consideration, there is mainly (by 8 out of 12 stations under study) a tendency to decrease in the values of the minimum average monthly river flow. The conducted studies lead us to the conclusion that the main factor causing changes in the low-water runoff of the rivers in the Lake Sevan basin in winter is the air temperature, or rather, its increase. It follows from this that when calculating and forecasting winter runoff, it is necessary to relate the values of runoff and air temperature.

Sub-Shrub Components Change the Soil Water Storage Response to Daily Precipitation and Air Temperature in the Loess Plateau

Soil water shortage has become a severe issue in ecological restoration and sustainable development in the Loess Plateau, facing the challenges of climate change and vegetation restoration. This study monitored the soil water content in surface soil (0–40 cm) with different sub-shrub component treatments, including the natural condition (NC), the canopy plus the roots (CR) and only the roots (OR), to analyze the change in soil water storage (∆W) and its response to precipitation (P) and air temperature (Ta) on a daily scale. P was the main factor controlling the daily ∆W, contributing 49–52% to the variation in the daily ∆W, and Ta only explained 6–21% of the variation. Minimum P amounts of 0.74–1.12 mm and maximum Ta of 29.09–32.00 °C were the thresholds required to increase soil water storage (W). Sub-shrub components showed significant influences on soil water conservation. We found that the ∆W hierarchy for each sub-shrub treatment was NC (1.73 mm) > CR (0.71 mm) > OR (0.56 mm) on rainy days and NC (−0.53 mm) < CR (−0.36 mm) < OR (−0.06 mm) on no-rain days. Additionally, the hierarchy of the rainwater retention rate was NC (26.43%) > OR (13.71%) > CR (4.58%). Thus, a canopy could increase infiltration and hugely consume soil water at the same time, while litter could weaken or offset the canopy’s effects and the roots promote infiltration with little evaporation loss.

Microclimate Characteristics in Mangrove Forest Areas in Padang City

Mangrove forest microclimate is the climatic conditions that occur in mangrove forests. The research aims to determine the conditions and characteristics of the microclimate (air temperature, air humidity and light intensity) in the mangrove forest area. The research location was chosen in the Taluak Buo mangrove forest area because the conditions and characteristics of the microclimate are not yet known. The data used is prime data obtained from direct measurements in the field. Measurements of several weather elements were carried out three times, namely in the morning (07.00-08.00 WIB), in the noon (12.00-13.00 WIB), and in the afternoon (17.00-18.00 WIB) for 7 days at three observation points using an anemometer instrument. The results obtained from this research in the Taluak Buo mangrove forest area in the Bungus Teluk Kabung sub-district are an average daily air temperature of 26.94°C in the forest, 27.94°C at the position on the edge of the forest and 28.6°C at outside the forest. The average difference in air temperature between inside the forest and outside the forest is between 1,22-2,14°C. On the other hand, the difference in air humidity is 5-6%. The average difference in sunlight intensity between inside the forest and outside the forest is between 3.923,44-22,462,66 lux. The air temperature and intensity of sunlight inside the forest is lower than at the edge and outside the forest, which is inversely proportional to the higher air humidity inside the forest.

Generalized Partially Functional Linear Model with Unknown Link Function

In existing models with an unknown link function, the issue of predictors containing both multiple functional data and multiple scalar data has not been studied. To fill this gap, we propose a generalized partially functional linear model, which not only models the relationship between multiple scalar and functional predictors and responses, but also automatically estimates the link function. Specifically, we use the functional principal component analysis method to reduce the dimensionality of functional predictors, estimate the regression coefficients using the maximum likelihood estimation method, estimate the link function using the method of local linear regression, iteratively obtain the final estimator, and establish the asymptotic normality of the estimator. The asymptotic normality is illustrated through simulation experiments. Finally, the proposed model is applied to study the influence of environmental, economic, and medical levels on life expectancy in China. In the study, functional predictors are the daily air quality index, temperature, and humidity of 58 cities in 2020, and scalar predictors are GDP and the number of beds in hospitals. The experimental results indicate that the unknown link function model has a smaller prediction error and better performance than both the model with the known link function and the model without a link function.

Antarctic daily mesoscale air temperature dataset derived from MODIS land and ice surface temperature

Knowledge about local air temperature variations and extremes in Antarctica is of large interest to many polar disciplines such as climatology, glaciology, hydrology, and ecology and it is a key variable to understand climate change. Due to the remote and harsh conditions of Antarctica’s environment, the distribution of air temperature observations from Automatic Weather Stations is notably sparse across the region. Previous studies have shown that satellite-derived land and ice surface temperatures can be used as a suitable proxy for air temperature. Here, we developed a daily near-surface air temperature dataset, AntAir ICE for terrestrial Antarctica and the surrounding ice shelves by modelling air temperature from MODIS skin temperature for the period 2003 to 2021 using a linear model. AntAir ICE has a daily temporal resolution and a gridded spatial resolution of 1 km2. AntAir ICE has a higher accuracy in reproducing in-situ measured air temperature when compared with the well-established climate re-analysis model ERA5 and a higher spatial resolution which highlights its potential for monitoring temperature patterns in Antarctica.

Dynamics and variability of microclimate in an unmanaged mountain forest after a bark beetle outbreak

Forest disturbances alter hydrological, energy, and microclimatic characteristics and their extents vary with the landscape characteristics of the terrain. We evaluated changes in evapotranspiration and latent heat rate in an unmanaged, central European mountain forest (Norway spruce) after a bark beetle-induced mortality of >75 % trees, and the spatial variability in microclimate during its natural regeneration. We observed that (1) the annual mean incident solar radiation to the treeless plots was 8.2 MJ m−2 day−1 (95 W m−2), being reduced by ∼45 % compared to its theoretical flux by high average cloudiness (70.6%) and short daily sunshine (4.3 hour day−1). The observed energy input to the forest floor beneath survived canopies (1.2 MJ m−2 day−1) was an order of magnitude lower than solar radiation to the canopy surface. (2) The mean annual evapotranspiration and the average annual latent heat rate decreased by 155 mm yr−1 and 385 MJ m−2 yr−1 (12 W m−2), respectively, after tree dieback. (3) The soil and air temperatures at individual plots correlated positively with the incident solar radiation and negatively with elevation. Mean soil and air temperatures exhibited similar lapse rates of 5.9–8.0 °C km−1 during the growing season (May-October). The energy input (positive relationship) and elevation (negative relationship) explained 74–83 % of the between-plot variability in mean soil and air temperatures and 51–77 % of their daily amplitudes during the growing season. (4) On sunny days, the observed soil and air temperatures were significantly lower (by ∼50 and 15 %, respectively) than ground surface temperature based on thermal satellite data (Landsat-8). The ground surface temperature explained 80–92 % of the observed variability in the soil and air temperatures. (5) The between-plot differences in daily soil and air temperatures and their amplitudes were from 24 to 70 % explained by solar radiation (positive correlation) and relative air humidity and wind speed (negative correlations).

Spatiotemporal prediction of daily air temperature using remote sensing and machine learning in Morocco

Spatiotemporal prediction of daily air temperature using remote sensing and machine learning in Morocco

Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions

Abstract. Despite covering only 3 % of the planet’s land surface, peatlands store 30 % of the planet’s terrestrial carbon. The net greenhouse gas (GHG) emissions from peatlands depend on many factors but primarily soil temperature, vegetation composition, water level and drainage, and land management. However, many peatland models rely on water levels to estimate CH4 exchange, neglecting to consider the role of CH4 transported to the atmosphere by vegetation. To assess the impact of vegetation on the GHG fluxes of peatlands, we have developed a new model, Peatland-VU-NUCOM (PVN). The PVN model is a site-specific peatland CH4 and CO2 emissions model, able to reproduce vegetation dynamics. To represent dynamic vegetation, we have introduced plant functional types and competition, adapted from the NUCOM-BOG model, into the framework of the Peatland-VU model, a peatland GHG emissions model. The new PVN model includes plant competition, CH4 diffusion, ebullition, root, shoot, litter, exudate production, belowground decomposition, and aboveground moss development under changing water levels and climatic conditions. Here, we present the PVN model structure and explore the model's sensitivity to environmental input data and the introduction of the new vegetation competition schemes. We evaluate the model against observed chamber data collected at two peatland sites in the Netherlands to show that the model is able to reproduce realistic plant biomass fractions and daily CH4 and CO2 fluxes. We find that daily air temperature, water level, harvest frequency and height, and vegetation composition drive CH4 and CO2 emissions. We find that this process-based model is suitable to be used to simulate peatland vegetation dynamics and CH4 and CO2 emissions.

Climate change impacts on streamflow, sediment load and carbon fluxes in the Lena River delta

Water and sediment supply are essential to the health of deltaic ecosystems. Diverse datasets were integrated to better understand how climate change is shifting the supply of water and sediment to the largest polar distributary channel pattern – the Lena River Delta. Here the increase in warming rate from an average air temperature is from 4.1 °C for the period 1950–99 to 6.1 °C during 2000–21, which is higher than in the adjacent polar regions. Streamflow and sediment yield entering the Lena Delta have increased since 1988 by 56.3 km3 and 6.1×106 t, respectively; meanwhile, the Lena River’s increases in water temperature in June, July–August and September were found to be as much as 1.1, 0.6 and 0.05 °C. These changes have a pronounced effect on sediment regimes in particular parts of the delta. Based on analyses of correlations between various hydroclimatic drivers and sediment concentration changes across particular distributaries of the Lena Delta extracted from Landsat datasets, bank degradation driven by thermal erosional processes (which are in turn related to air and soil temperature increases) is proved to be the primary factor of the sediment regime in the delta. The study also highlights that sediment load changes are sensitive to wind speed due to remobilization of bottom sediment. Sums of daily air temperature and wind speed over 3 days are correlated with sediment concentration changes in the delta. The results also indicate that carbon transport across the delta (both POC and DOC) depends on sediment transport conditions and water discharge and might increase by up to 10 %. We conclude that the Lena Delta can be recognized as the global hot spot in terms of the hydrological consequences of climate change, which is altering sediment regimes, stream hydromorphology and carbon transport.

Mitigating Heat Exposure: Exploring the Role of Knowledge, Risk Perception, and Precautionary Behavior

Increasing ambient temperatures due to climate change have significantly impacted workers’ physical and mental workloads, affecting their health and safety. Promoting precautionary behaviors, especially among outdoor workers, is an effective strategy widely recommended for addressing these challenges. Precautionary behavior represents an essential protective mechanism that can be adopted at the individual level. This study explores the connections among Occupational Safety and Health (OSH), risk perception, and precautionary behavior. Descriptive statistics and Structural Equation Modeling with the Partial Least Squares approach were used to analyze the data. This study was conducted at two forest management units in Central Java and East Java, Indonesia, where daily air temperatures are relatively high. The research involved 100 respondents, comprising 50 indoor and 50 outdoor workers. The findings of this investigation reveal that OSH-related knowledge among indoor workers predicts their precautionary behavior. Nevertheless, according to the model, attempts to improve precautionary behavior through knowledge enhancement are estimated to be unsuccessful among outdoor workers. Nonetheless, OSH-related knowledge can facilitate workers in developing a more realistic perception, especially regarding the “dread risk factor” among forestry workers, which can positively impact their precautionary behavior. Keywords: forestry worker, heat exposure, mediation analysis, occupational health, psychometrics paradigm

Physiological Response of Tomato and Cucumber Plants to Micro-Spray in High-Temperature Environment: A Scientific and Effective Means of Alleviating Crop Heat Stress

Drought and heat stresses are severe threats to crop production and food security in arid and semi-arid regions of the globe, especially during the summer. This study investigates the effects of micro-spray on micro-climatic and physiological characteristics of cucumber and tomato plants for four growing seasons (two growing seasons each (cucumber: 2017 and 2018 and tomato: 2021 and 2022)). The experiment was conducted in a Venlo-type greenhouse where plants were irrigated with a combined micro-spray and drip irrigation (MSDI) system (1 min for tomato (seven times a day) and 2 mins (four times a day) for cucumber) and drip irrigation (DI). Both plants’ growth and physiological parameters were recorded at the end of the experiment. Moreover, the experimental results showed that plant height and stem diameter increased under MSDI in all seasons. In addition, a micro-spray duration of 2 min decreased the average daily air temperature (Ta) and leaf temperature (T1) by 0.8 °C and 4.9 °C, respectively, and increased the average daily relative humidity (RH) by 4.3%. However, a micro-spray duration of 1 min reduced the average daily Ta and T1 by 0.76 °C and 4.6 °C, respectively, but the increase in RH (2.7%) was much lower than that in the single micro-spray duration of 2 min. In addition, the net leaf photosynthetic rate (Pn), the effective quantum yield of PSII (ΦPSII), and the stomatal conductance (Gs) of both plants increased under MSDI compared with DI. A K-means analysis showed that MSDI could eliminate the adverse effects of sunlight stress on cucumbers and tomatoes. MSDI increased the yield of cucumber and tomato plants by 18.9% and 40.4%, respectively. The fruit weight of cucumber (2017) and tomato (2021) under MSDI did not increase significantly compared to DI, which indicates that MSDI mitigated heat stress, prevented flowers from being burned, and increased the number of fruits. The results also suggested that the total soluble solids in the cucumbers and tomatoes showed no significant differences (p > 0.05) between the two treatments in four seasons. In conclusion, the MSDI system can be an appropriate strategy for the irrigation needs and climate control of plants grown in greenhouses during the hot season.

Stream groundwater inputs generate fine‐scale variation in brook trout phenology and growth across a warming landscape

Abstract Climate change is increasing global atmospheric temperatures, which can reduce abundance and cause range shifts in species that are sensitive to warming. However, fine‐scale thermal heterogeneity can drive highly variable local responses to climate change, especially in freshwater environments that differ in groundwater inputs and geomorphology. We used temperature data collected during 2012–2021 from 10 small, pristine streams in eastern Canada to characterise thermal variation at a small spatial scale (~25 km2). We then used relationships between daily air and stream temperatures to reconstruct stream temperature since 1980, and assessed how thermal variation influenced the phenology and growth of brook trout (Salvelinus fontinalis). Air–stream temperature relationships varied considerably among streams despite their close proximity, with predicted summer temperatures differing up to 9.5°C between warmer rainfall‐dominated streams and cooler groundwater‐dominated streams. Rainfall‐dominated streams warmed more than twice as fast as groundwater‐dominated streams across all seasons since 1980, with nearly four‐fold differences in rates of warming evident during summer months. Fine‐scale thermal heterogeneity also shaped brook trout phenology, as juveniles in rainfall‐dominated streams were estimated to hatch and emerge much later (~70 and 40 days, respectively) and experience faster phenological shifts than in groundwater‐dominated streams. Relationships between juvenile brook trout size and accumulated degree‐days were positive, but slopes differed over two‐fold and did not vary systematically based on stream hydrology, suggesting more idiosyncratic impacts of warming on early growth. Collectively, our study illustrates how species responses to climate change in freshwater environments can be consistent in direction but vary substantially in magnitude owing to the influence of groundwater. Future climate change is likely to increase the thermal stress experienced by brook trout populations in warmer rainfall‐dominated streams, while potentially benefiting those in groundwater‐dominated streams where current temperatures are often suboptimal. Observed differences in the rates and ecological impacts of warming among streams suggest that fine‐scale thermal variation must be considered when forecasting effects of future climate change on stream fish population dynamics.