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1.
Identification of the most sensitive hydrological regions to a changing climate is essential to target adaptive management strategies. This study presents a quantitative assessment of spatial patterns, inter‐annual variability and climatic sensitivity of the shape (form) and magnitude (size) of annual river/stream water temperature regimes across England and Wales. Classification of long‐term average (1989–2006) annual river (air) temperature regime dynamics at 88 (38) stations within England and Wales identified spatially differentiable regions. Emergent river temperature regions were used to structure detailed hydroclimatological analyses of a subset of 38 paired river and air temperature stations. The shape and magnitude of air and water temperature regimes were classified for individual station‐years; and a sensitivity index (SI, based on conditional probability) was used to quantify the strength of associations between river and air temperature regimes. The nature and strength of air–river temperature regime links differed between regions. River basin properties considered to be static over the timescale of the study were used to infer modification of air–river temperature links by basin hydrological processes. The strongest links were observed in regions where groundwater contributions to runoff (estimated by basin permeability) were smallest and water exposure time to the atmosphere (estimated by basin area) was greatest. These findings provide a new large‐scale perspective on the hydroclimatological controls driving river thermal dynamics and, thus, yield a scientific basis for informed management and regulatory decisions concerning river temperature within England and Wales. © 2013 The Authors. Hydrological Processes published by John Wiley & Sons, Ltd.  相似文献   

2.
Water temperature dynamics in High Arctic river basins   总被引：2，自引：0，他引：2  
Despite the high sensitivity of polar regions to climate change and the strong influence of temperature upon ecosystem processes, contemporary understanding of water temperature dynamics in Arctic river systems is limited. This research gap was addressed by exploring high‐resolution water column thermal regimes for glacier‐fed and non‐glacial rivers at eight sites across Svalbard during the 2010 melt season. Mean water column temperatures in glacier‐fed rivers (0.3–3.2 °C) were lowest and least variable near the glacier terminus but increased downstream (0.7–2.3 °C km–1). Non‐glacial rivers, where discharge was sourced primarily from snowmelt runoff, were warmer (mean: 2.9–5.7 °C) and more variable, indicating increased water residence times in shallow alluvial zones and increased potential for atmospheric influence. Mean summer water temperature and the magnitude of daily thermal variation were similar to those of some Alaskan Arctic rivers but low at all sites when compared with alpine glacierized environments at lower latitudes. Thermal regimes were correlated strongly (p < 0.01) with incoming short‐wave radiation, air temperature, and river discharge. Principal drivers of thermal variability were inferred to be (i) water source (i.e. glacier melt, snowmelt, groundwater); (ii) exposure time to the atmosphere; (iii) prevailing meteorological conditions; (iv) river discharge; (v) runoff interaction with permafrost and buried ice; and (vi) basin‐specific geomorphological features (e.g. channel morphology). These results provide insight into the potential changes in high‐latitude river systems in the context of projected warming in polar regions. We hypothesize that warmer and more variable temperature regimes may prevail in the future as the proportion of bulk discharge sourced from glacial meltwater declines and rivers undergo a progressive shift towards snow water and groundwater sources. Importantly, such changes could have implications for aquatic species diversity and abundance and influence rates of ecosystem functioning in high‐latitude river systems. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

3.
ABSTRACT

The article discusses the range and course of changes in the thermal regime of 14 rivers in Poland over the period 1961–2010. Eleven rivers are located in the Central European Plain, and the others flow in the foothills of the Carpathians Mountains. Statistical analyses take into consideration the results of daily measurements of water temperature carried out at 16 hydrological stations by the Institute of Meteorology and Water Management—National Research Institute. In the first part of the analysed period (1961–1986) water temperature in most rivers declined in relation to its mean value for the entire study period (1961–2010). In 1987 there was a reverse trend: the temperature started rising. The fastest increase in water temperature was recorded in the western part of the study area, and it became slower towards the east. In the southern part of the study area (the foothills) changes of that kind were not observed. The mean yearly temperature of fluvial waters in the Central European Plain showed a positive trend, ranging from 0.17 to 0.27°C (10 years)-1, whereas it did not change in the rivers in the foothills of the Carpathians Mountains. Its fastest rise was recorded in spring, and it reached from 0.08 to 0.43°C (10 years)-1. The increase in water temperature correlated strongly with rising air temperature. The temperature of river waters in the lowlands is believed to be a good indicator of climatic changes.
Editor M.C. Acreman Associate editor T. Okruszko  相似文献   

4.
Currently, the distribution areas of aquatic species are studied by using air temperature as a proxy of water temperature, which is not available at a regional scale. To simulate water temperature at a regional scale, a physically based model using the equilibrium temperature concept and including upstream‐downstream propagation of the thermal signal is proposed. This model, called Temperature‐NETwork (T‐NET), is based on a hydrographical network topology and was tested at the Loire basin scale (105 km2). The T‐NET model obtained a mean root mean square error of 1.6 °C at a daily time step on the basis of 128 water temperature stations (2008–2012). The model obtained excellent performance at stations located on small and medium rivers (distance from headwater <100 km) that are strongly influenced by headwater conditions (median root mean square error of 1.8 °C). The shading factor and the headwater temperature were the most important variables on the mean simulated temperature, while the river discharge influenced the daily temperature variation and diurnal amplitude. The T‐NET model simulates specific events, such as temperature of the Loire during the floods of June 1992 and the thermal regime response of streams during the heatwave of August 2003, much more efficiently than a simple point‐scale heat balance model. The T‐NET model is very consistent at a regional scale and could easily be transposed to changing forcing conditions and to other catchments. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

5.
Flow regulation is widely known to modify the thermal regime of rivers. Here, we examine the sensitivity of an empirical approach, the Equilibrium Temperature Concept (ETC), to detect both the effects of hydraulic infrastructures on the annual thermal cycle and the recovery of the thermal equilibrium with the atmosphere. Analysis was undertaken in a Pyrenean river (the Noguera Pallaresa, Ebro basin) affected by a series of reservoirs and hydropower plants. Equilibrium temperature (Te) is defined as the water temperature (Tw) at which the sum of all heat fluxes is zero. Based on the assumption of a linear relationship between Te and Tw, we identified changes in the Te–Tw regression slope, used as an indicator of a thermal alteration in river flow. We also assessed the magnitude of the alteration by examining the regression slope and its statistical significance. Variations in the regression parameters were used as indicators of the influence of factors other than atmospheric conditions on water temperature. Observed Tw showed a linear relationship with Te at all river stations. However, the slopes of the Te–Tw relationship appeared to be lower in the reaches downstream from hydraulic infrastructures, particularly below large dams. A seasonal analysis indicated that Te–Tw relationships had higher slopes and lower p‐values during autumn, while no significant differences were found at other seasons. Although thermal characteristics did not strongly depend on atmospheric conditions downstream of hydraulic infrastructures, the river recovered to pre‐alteration conditions with distance downstream, indicating the natural tendency of water to attain thermal equilibrium with the atmosphere. Accepting associated uncertainties, mostly because of the quality of the data and the lack of consideration of other factors influencing the thermal regime (e.g. discharge), ETC appears to be a simple and effective method to identify thermal alterations in regulated rivers. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

6.
River water temperature is a common target of water quality models at the watershed scale, owing to its principal role in shaping biogeochemical processes and in stream ecology. Usually, models include physically‐based, deterministic formulations to calculate water temperatures from detailed meteorological information, which usually comes from meteorological stations located far from the river reaches. However, alternative empirical approaches have been proposed, that usually depend on air temperature as master variable. This study explored the performance of a semidistributed water quality application modelling river water temperature in a Mediterranean watershed, using three different approaches. First, a deterministic approach was used accounting for the different heat exchange components usually considered in water temperature models. Second, an empirical approximation was applied using the equilibrium temperature concept, assuming a linear relationship with air temperature. And third, a hybrid approach was constructed, in which the temperature equilibrium concept and the deterministic approach were combined. Results showed that the hybrid approach gave the best results, followed by the empirical approximation. The deterministic formulation gave the worst results. The hybrid approach not only fitted daily river water temperatures, but also adequately modelled the daily temperature range (maximum–minimum daily temperature). Other river water features directly dependent on water temperature, such as river intrusion depth in lentic systems (i.e. the depth at which the river inflow plunges to equilibrate density differences with lake water), were also correctly modelled even at hourly time steps. However, results for the different heat fluxes between river and atmosphere were very unrealistic. Although direct evidence of discrepancies between meteorological drivers measured at the meteorological stations and the actual river microclimate was not found, the use of models including empirical or hybrid formulations depending mainly on air temperature is recommended if only meteorological data from locations far from the river reaches are available. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

7.
There is increasing demand for models that can accurately predict river temperature at the large spatial scales appropriate to river management. This paper combined summer water temperature data from a strategically designed, quality controlled network of 25 sites, with recently developed flexible spatial regression models, to understand and predict river temperature across a 3,000 km2 river catchment. Minimum, mean and maximum temperatures were modelled as a function of nine potential landscape covariates that represented proxies for heat and water exchange processes. Generalised additive models were used to allow for flexible responses. Spatial structure in the river network data (local spatial variation) was accounted for by including river network smoothers. Minimum and mean temperatures decreased with increasing elevation, riparian woodland and channel gradient. Maximum temperatures increased with channel width. There was greater between‐river and between‐reach variability in all temperature metrics in lower‐order rivers indicating that increased monitoring effort should be focussed at these smaller scales. The combination of strategic network design and recently developed spatial statistical approaches employed in this study have not been used in previous studies of river temperature. The resulting catchment scale temperature models provide a valuable quantitative tool for understanding and predicting river temperature variability at the catchment scales relevant to land use planning and fisheries management and provide a template for future studies.  相似文献   

8.
This paper investigates three categories of models that are derived from the equilibrium temperature concept to estimate water temperatures in the Loire River in France and the sensitivity to changes in hydrology and climate. We test the models' individual performances for simulating water temperatures and assess the variability of the thermal responses under the extreme changing climate scenarios that are projected for 2081–2100. We attempt to identify the most reliable models for studying the impact of climate change on river temperature (Tw). Six models are based on a linear relationship between air temperatures (Ta) and equilibrium temperatures (Te), six depend on a logistic relationship, and six rely on the closure of heat budgets. For each category, three approaches that account for the river's thermal exchange coefficient are tested. In addition to air temperatures, an index of day length is incorporated to compute equilibrium temperatures. Each model is analysed in terms of its ability to simulate the seasonal patterns of river temperatures and heat peaks. We found that including the day length as a covariate in regression‐based approaches improves the performance in comparison with classical approaches that use only Ta. Moreover, the regression‐based models that rely on the logistic relationship between Te and Ta exhibit root mean square errors comparable (0.90 °C) with those obtained with a classical five‐term heat budget model (0.82 °C), despite a small number of required forcing variables. In contrast, the regressive models that are based on a linear relationship Te = f(Ta) fail to simulate the heat peaks and are not advisable for climate change studies. The regression‐based approaches that are based on a logistic relationship and the heat balance approaches generate notably similar responses to the projected climate changes scenarios. This similarity suggests that sophisticated thermal models are not preferable to cruder ones, which are less time‐consuming and require fewer input data. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

9.
This paper presents the results of an investigation into the problems associated with using downscaled meteorological data for hydrological simulations of climate scenarios. The influence of both the hydrological models and the meteorological inputs driving these models on climate scenario simulation studies are investigated. A regression‐based statistical tool (SDSM) is used to downscale the daily precipitation and temperature data based on climate predictors derived from the Canadian global climate model (CGCM1), and two types of hydrological model, namely the physically based watershed model WatFlood and the lumped‐conceptual modelling system HBV‐96, are used to simulate the flow regimes in the major rivers of the Saguenay watershed in Quebec. The models are validated with meteorological inputs from both the historical records and the statistically downscaled outputs. Although the two hydrological models demonstrated satisfactory performances in simulating stream flows in most of the rivers when provided with historic precipitation and temperature records, both performed less well and responded differently when provided with downscaled precipitation and temperature data. By demonstrating the problems in accurately simulating river flows based on downscaled data for the current climate, we discuss the difficulties associated with downscaling and hydrological models used in estimating the possible hydrological impact of climate change scenarios. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

10.
Fish habitat and aquatic life in rivers are highly dependent on water temperature. Therefore, it is important to understand andto be able to predict river water temperatures using models. Such models can increase our knowledge of river thermal regimes as well as provide tools for environmental impact assessments. In this study, artificial neural networks (ANNs) will be used to develop models for predicting both the mean and maximum daily water temperature. The study was conducted within Catamaran Brook, a small drainage basin tributary to the Miramichi River (New Brunswick, Canada). In total, eight ANN models were investigated using a variety of input parameters. Of these models, four predicted mean daily water temperature and four predicted maximum daily water temperature. The best model for mean daily temperature had eight input parameters: minimum, maximum and mean air temperatures of the current day and those of the preceding day, the day of year and the water level. This model had an overall root‐mean‐square error (RMSE) of 0·96 °C, a bias of 0·26 °C and a coefficient of determination R2 = 0·971. The model that best predicted maximum daily water temperature was similar to the first model but excluded mean daily air temperature. Good results were obtained for maximum water temperatures with an overall RMSE of 1·18 °C, a bias of 0·15 °C and R2 = 0·961. The results of ANN models were similar to and/or better than those observed from the literature. The advantages of artificial neural networks models in modelling river water temperature lie in their simplicity of use, their low data requirement and their good performance, as well as their flexibility in allowing many input and output parameters. Copyright © 2008 Crown in the right of Canada and John Wiley & Sons, Ltd.  相似文献   

11.
Comprehensively assessing the hydrological alteration of river flows is a prerequisite for the scientific management, protection, and restoration of rivers. The range of variability approach (RVA), which is based on indicators of hydrological alteration (IHAs), is a widely used method to evaluate hydrological alteration. However, the RVA only considers the frequency of each IHA, neglecting the equally important temporal order of these IHAs. The order of IHA event can be reflected by its periodicity. On the basis of the RVA, in this study, we propose a revised RVA that considers both the frequency and periodicity of IHAs. In the revised RVA, first, the periodic time of each IHA is identified; next, the periodicity alteration (P) of river flow is calculated by comparing the periodic times of the pre‐impact‐period and post‐impact‐period IHAs; finally, P and the frequency alteration(D) in traditional RVA are incorporated into a single index (H) to reflect the overall hydrological alteration. A case study of the Xi Dayang (XDY) Reservoir and rearranged flow suggests that the traditional RVA underestimates hydrological alterations because it neglects the alteration of periodicity. Compared with the traditional RVA and its alternatives, the revised RVA could give a more comprehensive representation of hydrological alteration caused by human and nature impacts. Thus, better protection of an ecosystem could be obtained by applying this method in the evaluation and management of water resources. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

12.
Abstract

Measurements made in the past few decades undeniably indicate change in the climate. The most visible sign of global climate change is air temperature, while less visible indicators include changes in river water temperatures. Changes in river temperature can significantly affect the environment, primarily the biosphere. The physical, biological and chemical characteristics of the river are directly affected by water temperature, although estimation of this relationship presents a complex problem. Although river temperature is influenced by hydrological and meteorological factors, the purpose of this study is to model daily water temperature using only one known parameter, mean air temperature. The relationship between the daily mean air and daily water temperature of the River Drava in Croatia is analysed using linear regression, stochastic modelling or nonlinear regression and multilayer perceptron (MLP) feed-forward neural networks. The results indicate that the MLP models are much better models which can be used for the estimation and prediction of daily mean river temperature.
Editor D. Koutsoyiannis; Associate editor M. Acreman  相似文献   

13.
Seven longitudinal water temperature tow surveys were conducted to attempt to identify the location of surface and subsurface river water exchanges along the length of the West River at the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut, Canada (74°55′ N, 109°35′ W). Water temperature data were collected using a calibrated thermistor with an accuracy of ±0.002 °C (resolution <0.00005 °C) along the river during July 2014 in conjunction with stable water isotope sampling to support the thermal data and to determine the extent of surface water mixing from different sources such as precipitation, snowmelt, and surface/subsurface water contributions to the river. Atmospheric conditions were found to be the main contributor to seasonal temperature variance in the river, whereas tributary inflows and residual channel snow also had important thermal effects to river temperatures. Residual channel snow was a sustained source of cold water during much of the 2014 summer season (June–August) and substantially offset downstream warming. The longitudinal temperature profiles indicate notable changes to the thermal state of the river, which are interpreted to be indicative of subsurface and surface water exchange through inputs of relatively cold or warm water. Broadly, surface inflows were found to provide warmer water relative to the West River, and contributed to downstream warming of the river, along with downstream enrichment of δD and δ18O. Subsurface inflows provided cooler water relative to the river, and contributed to downstream depletion of δD and δ18O and downstream cooling of river temperatures. These results demonstrate that localized changes in river temperature, in conjunction with isotopic tracers, can be used to track channel–slope water interactions in Arctic hydrological systems, work previously limited to alpine and temperate settings.  相似文献   

14.
Successful applications of stochastic models for simulating and predicting daily stream temperature have been reported in the literature. These stochastic models have been generally tested on small rivers and have used only air temperature as an exogenous variable. This study investigates the stochastic modelling of daily mean stream water temperatures on the Moisie River, a relatively large unregulated river located in Québec, Canada. The objective of the study is to compare different stochastic approaches previously used on small streams to relate mean daily water temperatures to air temperatures and streamflow indices. Various stochastic approaches are used to model the water temperature residuals, representing short‐term variations, which were obtained by subtracting the seasonal components from water temperature time‐series. The first three models, a multiple regression, a second‐order autoregressive model, and a Box and Jenkins model, used only lagged air temperature residuals as exogenous variables. The root‐mean‐square error (RMSE) for these models varied between 0·53 and 1·70 °C and the second‐order autoregressive model provided the best results. A statistical methodology using best subsets regression is proposed to model the combined effect of discharge and air temperature on stream temperatures. Various streamflow indices were considered as additional independent variables, and models with different number of variables were tested. The results indicated that the best model included relative change in flow as the most important streamflow index. The RMSE for this model was of the order of 0·51 °C, which shows a small improvement over the first three models that did not include streamflow indices. The ridge regression was applied to this model to alleviate the potential statistical inadequacies associated with multicollinearity. The amplitude and sign of the ridge regression coefficients seem to be more in agreement with prior expectations (e.g. positive correlation between water temperature residuals of different lags) and make more physical sense. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

15.
J.J. Dick  D. Tetzlaff  C. Soulsby 《水文研究》2015,29(14):3098-3111
We monitored temperatures in stream water, groundwater and riparian wetland surface water over 18 months in a 3.2‐km2 moorland catchment in the Scottish Highlands. The stream occupies a glaciated valley, aligned east–west. It has three main headwater tributaries with a large north facing catchment, a south facing catchment and the smallest east facing headwater. The lower catchment sampling locations begin after the convalescence of all three headwaters. Much of the stream network is fringed by riparian peatlands. Stream temperatures are mainly regulated by energy exchanges at the air–water interface. However, they are also influenced by inflows from the saturated riparian zone, where surface water source areas are strongly connected with the stream network. Consequently, the spatial distribution of stream temperatures exhibits limited variability. Nevertheless, there are significant summer differences between the headwaters, despite their close proximity to each other. This is consistent with aspect (and incident radiation), given the south and east facing headwaters having higher temperatures. The largest, north‐facing sub‐catchment shows lower summer diurnal temperature variability, suggesting that lower radiation inputs dampen temperature extremes. Whilst stream water temperature regimes in the lower catchment exhibit little change along a 1‐km reach, they are similar to those in the largest headwater; probably reflecting size and comparable catchment aspect and hydrological flow paths. Our results suggest that different parts of the channel network and its connected wetlands have contrasting sensitivity to higher summer temperatures. This may be important in land management strategies designed to mitigate the impacts of projected climatic warming. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

16.
Climate change is altering river temperature regimes, modifying the dynamics of temperature‐sensitive fishes. The ability to map river temperature is therefore important for understanding the impacts of future warming. Thermal infrared (TIR) remote sensing has proven effective for river temperature mapping, but TIR surveys of rivers remain expensive. Recent drone‐based TIR systems present a potential solution to this problem. However, information regarding the utility of these miniaturised systems for surveying rivers is limited. Here, we present the results of several drone‐based TIR surveys conducted with a view to understanding their suitability for characterising river temperature heterogeneity. We find that drone‐based TIR data are able to clearly reveal the location and extent of discrete thermal inputs to rivers, but thermal imagery suffers from temperature drift‐induced bias, which prevents the extraction of accurate temperature data. Statistical analysis of the causes of this drift reveals that drone flight characteristics and environmental conditions at the time of acquisition explain ~66% of the variance in TIR sensor drift. These results shed important light on the factors influencing drone‐based TIR data quality and suggest that further technological development is required to enable the extraction of robust river temperature data. Nonetheless, this technology represents a promising approach for augmenting in situ sensor capabilities and improved quantification of advective inputs to rivers at intermediate spatial scales between point measurements and “conventional” airborne or satellite remote sensing.  相似文献   

17.
Prevailing theory suggests that stream temperature warms asymptotically in a downstream direction, beginning at the temperature of the source in the headwaters and levelling off downstream as it converges to match meteorological conditions. However, there have been few empirical examples of longitudinal patterns of temperature in large rivers due to a paucity of data. We constructed longitudinal thermal profiles (temperature vs distance) for 53 rivers in the Pacific Northwest (USA) using an extensive data set of remotely sensed summertime river temperatures and classified each profile into one of five patterns of downstream warming: asymptotic (increasing then flattening), linear (increasing steadily), uniform (not changing), parabolic (increasing then decreasing), or complex (not fitting other classes). We evaluated (1) how frequently profiles warmed asymptotically downstream as expected, and (2) whether relationships between river temperature and common hydroclimatic variables differed by profile class. We found considerable diversity in profile shape, with 47% of rivers warming asymptotically and 53% having alternative profile shapes. Water temperature did not warm substantially over the course of the river for coastal parabolic and uniform profiles, and for some linear and complex profiles. Profile classes showed no clear geographical trends. The degree of correlation between river temperature and hydroclimatic variables differed among profile classes, but there was overlap among classes. Water temperature in rivers with asymptotic or parabolic profiles was positively correlated with August air temperature, tributary temperature and velocity, and negatively correlated with elevation, August precipitation, gradient and distance upstream. Conversely, associations were less apparent in rivers with linear, uniform or complex profiles. Factors contributing to the unique shape of parabolic profiles differed for coastal and inland rivers, where downstream cooling was influenced locally by climate or cool water inputs, respectively. Potential drivers of shape for complex profiles were specific to each river. These thermal patterns indicate diverse thermal habitats that may promote resilience of aquatic biota to climate change. Without this spatial context, climate change models may incorrectly estimate loss of thermally suitable habitat. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

18.
Stephen B. Shaw 《水文研究》2017,31(21):3729-3739
There remains continued use of non‐linear, logistic regression models for predicting water temperature from air temperature. A dominant feature of these non‐linear models is an upper bound on river water temperature. This upper bound is often attributed to a large increase in evaporative cooling at high air temperatures, but the exact conditions under which such an increase may occur have not been thoroughly explored. To better understand the appropriateness of the non‐linear model for predicting river water temperatures, it is essential to understand the physical basis for the upper bound and when it should and should not be included in the statistical model. This paper applies and validates an energy balance model against 8 river systems spread across different climate regions of the United States. The energy balance model is then used to develop a diagram relating vapour pressure deficit and air temperature to water temperature. With knowledge of present or future vapour pressure deficit (difference between saturation and actual vapour content in the atmosphere) conditions in a given climate, the diagram can be used to predict the likelihood of an upper bound in the air–water temperature relationship. This investigation offers a fundamental physical explanation of the most appropriate form of statistical models that should be used for predicting future water temperature from air temperature in different geographic regions with different climate conditions. In general, climatic regions that have only a slight increase in vapour pressure deficit with increasing air temperature (typically humid regions) would not be expected to have an upper bound. Conversely, climatic regions in which vapour pressure deficit sharply increases with increasing air temperature (typically arid regions) would be expected to have an upper bound.  相似文献   

19.
Changes in water temperature can have important consequences for aquatic ecosystems, with some species being sensitive even to small shifts in temperature during some or all of their life cycle. While many studies report increasing regional and global air temperatures, evidence of changes in river water temperature has, thus far, been site specific and often from sites heavily influenced by human activities that themselves could lead to warming. Here we present a tiered assessment of changing river water temperature covering England and Wales with data from 2773 locations. We use novel statistical approaches to detect trends in irregularly sampled spot measurements taken between 1990 and 2006. During this 17‐year period, on average, mean water temperature increased by 0.03 °C per year (±0.002 °C), and positive changes in water temperature were observed at 2385 (86%) sites. Examination of catchments where there has been limited human influence on hydrological response shows that changes in river flow have had little influence on these water temperature trends. In the absence of other systematic influences on water temperature, it is inferred that anthropogenically driven climate change is driving some of this trend in water temperature. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.  相似文献   

20.
《水文科学杂志》2013,58(3):640-655
Abstract

Water temperature is an important abiotic variable in aquatic habitat studies and may be one of the factors limiting the potential fish habitat (e.g. salmonids) in a stream. Stream water temperatures are modelled using statistical approaches with air temperature and streamflow as exogenous variables in the Nivelle River, southern France. Two different models are used to model mean weekly maximum temperature data: a non-parametric approach, the k-nearest neighbours method (k-NN) and a parametric approach, the periodic autoregressive model with exogenous variables (PARX). The k-NN is a data-driven method, which consists of finding, at each point of interest, a small number of neighbours nearest to this value, and the prediction is estimated based on these neighbours. The PARX model is an extension of commonly-used autoregressive models in which parameters are estimated for each period within the years. Different variants of air temperature and flow are used in the model development. In order to test the performance of these models, a jack-knife technique is used, whereby model goodness of fit is assessed separately for each year. The results indicate that both models give good performances, but the PARX model should be preferred, because of its good estimation of the individual weekly temperatures and its ability to explicitly predict water temperature using exogenous variables.  相似文献   

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