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Optimization methodology for a river temperature monitoring network for the characterization of fish thermal habitat
Abstract
A methodology for planning an optimized river water temperature monitoring network is presented. The methodology is based on sampling of the physio-climatic variability of the region to be monitored. Physio-climatic metrics are selected to describe the study region, based on principal component analysis. The sites to be monitored are then identified based on a k-means clustering in the multidimensional space defined by the selected metrics. The methodology is validated on an existing dense water temperature network in Haute-Savoie, France. Different configurations of more or less dense network scenarios are evaluated by assessing their ability to estimate water temperature indices at ungauged locations. An optimized network containing 83 sites is found to provide satisfactory estimations for seven ecologically and biologically meaningful thermal indices defined to characterize brown trout thermal habitat.
... In the present study, we investigated patterns of offspring survival at egg stage in Salmo trutta, based on parental genotypes-using markers related to original MED and ATL lineages-in interaction with river temperature to detect potential GxE interactions. Selection can indeed act very strongly during early stages of development in salmonid species, notably as a function of temperature (Huuskonen et al., 2003;Ojanguren & Braña, 2003;Régnier et al., 2013), a factor that can show contrast at small scales (Brown & Hannah, 2008;Daigle et al., 2016). Offspring from ATL lineage are expected to be adapted to relatively warm temperatures (8-10°C) for prehatching survival as already demonstrated (Jungwirth & Winkler, 1984;Ojanguren & Braña, 2003;Régnier et al., 2013), a thermal range often encountered in their distribution area. ...
... Offspring from ATL lineage are expected to be adapted to relatively warm temperatures (8-10°C) for prehatching survival as already demonstrated (Jungwirth & Winkler, 1984;Ojanguren & Braña, 2003;Régnier et al., 2013), a thermal range often encountered in their distribution area. For the MED lineage, which evolved experiencing a wider range of contrasting temperatures (Daigle et al., 2016), we hypothesize that their offspring should at least outperform ATL offspring in cold water rate (4-6°C), since ATL offspring display very low survival at such temperatures (Ojanguren & Braña, 2003;Régnier et al., 2013). To assess the real importance of temperature on postzygotic selection, our experiment was performed directly in natural environments where substantial temperature contrasts are observed during winter (Burt et al., 2011). ...
... Temperature variation range itself is very heterogeneous at different spatial and temporal scales in mountain hydrosystems (Brown & Hannah, 2008;Daigle et al., 2016). It was also the case on our field study sites, wherein the three rivers were connected, within a few kilometers, but presented contrasted thermal regimes. ...
Allopatric gene pools can evolve in different directions through adaptive and non‐adaptive processes and are therefore a source of intraspecific diversity. The connection of these previously isolated gene pools through human intervention can lead to intraspecific diversity loss, through extirpation of native populations or hybridization. However, the mechanisms leading to these situations are not always explicitly documented and are thus rarely used to manage intraspecific diversity. In particular, genotype by environment (GxE) interactions can drive post‐zygotic reproductive isolation mechanisms that may result in a mosaic of diversity patterns, depending on the local environment. We test this hypothesis using a salmonid species (Salmo trutta) in the Mediterranean (MED) area, where intensive stocking from non‐native Atlantic (ATL) origins has led to various outcomes of hybridization with the native MED lineage, going from MED resilience to total extirpation via full hybridization. We investigate patterns of offspring survival at egg stage in natural environments, based on parental genotypes in interaction with river temperature, to detect potential GxE interactions. Our results show a strong influence of maternal GxE interaction on embryonic survival, mediated by maternal effect through egg size, and a weak influence of paternal GxE interaction. In particular, when egg size is large and temperature is cold, the survival rate of offspring originating from MED females is three times higher than that of ATL females offspring. Because river temperatures show contrast at small scale, this cold adaptation for MED females offspring constitutes a potent post‐zygotic mechanism to explain small‐scale spatial heterogeneity in diversity observed in MED areas where ATL fish have been stocked. It also indicates that management efforts could be specifically targeted at the environments that actively favor native intraspecific diversity through eco‐evolutionary processes such as post‐zygotic selection.
... Currently, relatively inexpensive water temperature loggers (Table 1) that allow for reasonably high-frequency (i.e. sub-hourly) measurements for periods longer than one year, with a precision that is typically between 0.2 and 0.5°C, have democratized water temperature monitoring since the early 1990s (Daigle et al., 2017;Isaak et al., 2017;Marsha et al., 2018;Meisner, 1990;Tonolla et al., 2019). Indeed, due to their inexpensiveness, sensor networks can cover broad spatial scales and have been used to develop and support large scale monitoring networks. ...
... Besides, they allow to gather long-term time series that can eventually provide evidence on the impacts of climate changes and changes in land use. To support the development of such networks, research on optimization methods and temperature interpolation between monitoring sites has increased over the last decade (Daigle et al., 2017;Guillemette et al., 2009;Isaak et al., 2014;Jackson et al., 2015). However, the need for manual sensor maintenance can be time-consuming; sensors need to be checked regularly for battery status, and performance and data downloaded, be cross-calibrated and be corrected for drift if required (e.g. ...
... Piccolroaz et al., 2016). These new developments are now allowing for accurate water temperature prediction across multiple scales using optimized sets of predictors, therefore reducing the amount of data needed, which can also potentially improve model transferability between regions (Daigle et al., 2010(Daigle et al., , 2017(Daigle et al., , 2019. ...
There is growing evidence that river temperatures are increasing under climate change, which is expected to be exacerbated by increased abstractions to satisfy human water demands. Water temperature research has experienced crucial advances, both in terms of developing new monitoring and modelling tools, as well as understanding the mechanisms of temperature feedbacks with biogeochemical and ecological processes. However, water practitioners and regulators are challenged with translating the widespread and complex technological, modelling and conceptual advances made in river temperature research into improvements in management practice.
This critical review provides a comprehensive overview of recent advances in the state-of-the-art monitoring and modelling tools available to inform ecological research and practice. In so doing, we identify pressing research gaps and suggest paths forward to address practical research and management challenges. The proposed research directions aim to provide new insights into spatio-temporal stream temperature dynamics and unravel drivers and controls of thermal river regimes, including the impacts of changing temperature on metabolism and aquatic biogeochemistry, as well as aquatic organisms. The findings of this review inform future research into ecosystem resilience in the face of thermal degradation and support the development of new management strategies cutting across spatial and temporal scales.
... Comparable knowledge about thermal regimes is needed, but has lagged because temperature data are not recorded at most flow gauges and datasets of annual records have been difficult to obtain from more than a few sites or watersheds (Orr et al. 2015;Isaak et al. 2018b). In recent years, data limitations have begun to ease with the advent of inexpensive, reliable temperature sensors and grassroots monitoring efforts are becoming common throughout much of Europe and North America (e.g., Hilderbrand et al. 2014;Trumbo et al. 2014;Nussl e et al. 2015;Daigle et al. 2016;Jackson et al. 2016;Mauger et al. 2016). ...
... Different, however, is the grassroots nature of temperature databases, which are growing because declining sensor costs are democratizing data acquisition efforts. Temperature sensors with multi-year data logging capacities cost U.S. $20-200, for example, are available from several manufacturers, and are easily deployed using standard protocols (Stamp et al. 2014), which has spawned an array of local monitoring networks by natural resource agencies and watershed councils in many countries (e.g., Trumbo et al. 2014;Daigle et al. 2016;Jackson et al. 2016;Mauger et al. 2016). ...
Thermal regimes of rivers and streams profoundly affect aquatic ecosystems but are poorly described and classified in many areas due to the limited availability of annual datasets from extensive and representative monitoring networks. By mining a new temperature database composed of >23,000 site records that spans the western United States, we extract annual monitoring records at 578 sites on perennial streams to describe regimes in this diverse region. Records were summarized using 34 metrics that described regime aspects related to magnitude, variation, frequency, duration, and timing. The metrics were used in a multivariate cluster analysis to classify streams into seven distinct regime types and in a principal components analysis (PCA) to examine patterns of redundancy among metrics. The PCA indicated that 2–5 orthogonal PC axes accounted for 74–89% of the variation in thermal regimes at the monitoring sites. Most of the variation in PC scores that defined the two dominant axes was in turn predictable from a suite of geospatial covariates in multiple linear regressions that included elevation, latitude, riparian canopy density, reach slope, precipitation, lake prevalence, and dam height. Our results have parallels to previous flow regime analyses that describe the utility of small numbers of PCs or allied metrics in regime characterization, and can be used to better understand and parsimoniously represent thermal regimes in the western United States.
... Understanding the physio-climatic attributes of watersheds is crucial to ensure that the study sites or monitoring networks adequately capture stream thermal regimes (Boyer et al. 2016;Steel et al. 2017). Daigle et al. (2016) proposed an optimization method that minimizes the number (and density) of temperature monitoring stations while maximizing the information gained. It was recommended to continue promoting and utilizing RIVTEMP, an optimized and highly collaborative water temperature monitoring network, to monitor Atlantic salmon rivers throughout the latitudinal range (see www.rivtemp.ca). ...
Climate change and human activities have dramatically affected all ecosystems inhabited by Atlantic salmon, causing drastic population declines. Change in river temperature dynamics (e.g. daily variability, frequency, and duration of summer maximum, warmer thermal regimes) is of special concern as it impacts growth rates, reproductive success, prey abundance and phenology, timing of migration, and ultimately survival. The Atlantic Salmon Research Joint Venture held a workshop to address the effects of climate change on freshwater habitats of Atlantic salmon and identify research gaps and priorities. Here we summarize the state of the science for three key themes identified by workshop participants: (1) Effects of climate change on in-river habitat conditions, (2) Physiological and behavioral responses of salmon to temperature, and (3) Population-level responses of salmon to climate change. The group highlighted the crucial importance of understanding and monitoring the links between river temperature dynamics and physiological requirements of Atlantic salmon across different life stages and habitat conditions, with a focus on freshwater life stages. Climate change will undoubtedly continue to affect instream habitats across all seasons and render challenging conditions for all freshwater Atlantic salmon life stages. Hence, we call for urgent interdisciplinary collaborations and partnerships among scientists and managers to address the pressing research gaps that require large-scale data integration across life cycle stages and ecosystems. More collaboration between scientists, managers, and interest groups is needed to ensure that fundamental science directly addresses the knowledge-action gap to enhance evidence-based decision-making and conservation.
... Marine areas are affected by many factors such as water temperature, atmospheric oscillations, precipitation, density changes, and current systems. In this direction, water temperature is among the most important variables monitored in many studies so far, especially in the sea, in different habitats such as rivers (Daigle et al., 2017) and lakes (Sharma et al., 2015). The data for detailed analysis of trends in the seawater temperature; climate change-induced negative or opportunities for the evaluation of different fields and sectors is important for hydrology, meteorology, agriculture, livestock, tourism, etc. ...
The increase of the world population, especially in the global competition, together with the increasing use of fossil fuel resources to meet energy needs, leads to more greenhouse gases (more than one CO2, methane etc.) emissions and the global climate crisis. In this process, changes in meteorological events such as temperature, precipitation, and wind, attract attention moreover but when considered as a whole, we know that these negative changes in the ecosystem negatively affect many living groups. Sea Surface Temperature (SST) as measured meteorologically is the most important environmental parameter where these changes are monitored and observed. It draws attention to the fact that changes in SST are not limited to living organisms as habitats, but also catalyze many chain reactions, especially socioeconomic impacts. Therefore, much of the work is devoted to forecasting studies to adapt to changing habitats and take the necessary precautions against potential risks. Feed-forward artificial neural networks have been commonly used to address these emerging needs. Artificial neural networks, which are a simple imitation of the human neurological system, have been used as an artificial intelligence method in forecasting problems due to their superior performance and not having the limitations of classical time series. In this study, the forecasting of the time series of monthly mean SST temperature obtained from Rize station between the years 2010 and 2020 is performed by using feed-forward artificial neural networks, and the forecasting performance of the corresponding time series is compared with many forecasting methods with different characteristics. The comparison of the methods used the mean square error and mean absolute percentage error criteria, which are commonly used in the forecasting literature. The analysis results showed that the analysis results obtained with the feed-forward artificial neural networks have the best prediction performance. As a result, it can be stated that the sea surface temperature can be forecasted with a very high accuracy using the feed-forward artificial neural networks. Öz: Küresel rekabet başta olmak üzere dünya nüfusundaki artış beraberinde enerji gereksinimlerini karşılamak adına fosil yakıt kaynaklarının kullanımının artmasına bu durum da sera gazlarının daha fazla salınmasına (daha fazla CO2, metan, vb.) ve küresel iklim değişikliğine neden olmaktadır. Bu süreçte başta sıcaklık, yağış ve rüzgâr gibi meteorolojik olaylarda yaşanan değişikler dikkat çekmekle birlikte, bir bütün olarak değerlendirildiğinde ekosistemde meydana gelen bu olumsuz değişimlerin pek çok canlı grubunu da olumsuz etkilediği bilinmektedir. Deniz suyu sıcaklığı bu değişikliğin izlendiği ve gözlemlendiği en önemli çevresel parametre olup, deniz suyunda meydana gelen değişimlerin sadece habitat olarak yaşam faaliyetlerini sürdüren canlılarla sınırlı kalmayıp, aynı zamanda sosyoekonomik etkileri başta olmak üzere zincirleme birçok reaksiyonu katalize etmesiyle de dikkat çekmektedir. Dolayısıyla değişen yaşam koşullarına adaptasyon sağlamada ve olası risklere karşı gereken önlemin alınması noktasında öngörü çalışmalarına büyük iş düşmektedir. Son zamanlarda meydana gelen bu gereksinimleri karşılamak adına ileri beslemeli yapay sinir ağlarından sıkça faydalanılmaktadır. İnsan nörolojik sisteminin basit bir taklidi olan yapay sinir ağları, klasik zaman serisi yöntemlerinin sahip olduğu kısıtlamalara sahip olmaması ve üstün performansı ile öngörü problemlerinde araştırmacılar tarafından sıklıkla kullanılan bir yapay zekâ yöntemi olarak kullanılmaktadır. Bu çalışmada Rize istasyonundan 2010-2020 yılları arasında elde edilen aylık ortalama deniz suyu sıcaklığına ait zaman serisinin tahmini ileri beslemeli yapay sinir ağları ile birlikte gerçekleştirilmiş olup ilgili zaman serisinin tahmin performansı farklı özelliklere sahip birçok öngörü yöntemi ile de karşılaştırılmıştır. Yöntemlerin karşılaştırılmasında öngörü literatüründe sıklıkla kullanılan hata kareler ortalaması karekök ve ortalama mutlak yüzdelik hata kriterleri kullanılmış olup elde edilen analiz sonuçlarına göre ileri beslemeli yapay sinir ağları ile elde edilen analiz sonuçlarının en uygun öngörü performansına sahip olduğu görülmüştür. Sonuç olarak; ileri beslemeli yapay sinir ağları yönteminden faydalanılarak deniz suyu yüzey sıcaklığının çok yüksek doğruluk oranı ile tahmin edilebileceği ifade edilmektedir. Anahtar kelimeler: Yapay sinir ağları, deniz yüzey sıcaklığı, küresel iklim değişikliği, Rize
... Due to the unavailability of sufficient streamflow data, this change may be underestimated after constructing the Koteshwar dam. Parameters in the Other category do not represent any pattern, which may be due to the unavailability of flow data for enough years (Patterson et al. 2020;Daigle et al. 2017). The parameters in the Other category of flow regime mainly represent skewness and variability in daily stream flows. ...
... Dataloggers have come down in price considerably over the last few decades providing sub hour recordings that operate for a year at a time (Daigle et al., 2017) allowing for networks such as RivTemp consisting of 478 permanent stations (Boyer et al., 2016). However, the claim that this has "democratised" river temperature science (Ouellet et al., 2020) may be premature as the 5 such networks described were all in the more developed Global North. ...
Hydropower interacts heavily with river temperature to; meet regulations, maximise profits, and maintain dam safety. Often the operational decisions that dictate this interaction are made without monitoring of river temperature, and so it is proposed that satellite remote sensing may provide a quasi-regular cost-effective method to improve this. This dissertation assesses the viability of using Google Earth Engine cloud computing and Landsat 8 Thermal Infrared satellite measurements to provide actionable insights for hydropower managers. The method was tested in three large rivers (the Saint John River in Canada, the Colorado River in the USA, and the Ganges in India) to assess transferability. No previous study has attempted to extract river temperature from multiple sites in a single study. Three different methods were tested to find the most accurate atmospheric correction algorithm for the task of river temperature measurement. The Statistical Mono-Window algorithm was found to produce the most accurate comparison to kinetic temperature loggers on the Saint John River (±2oc) with a R2 value of 0.96 (n=40, p<0.001). However, this method was not transferable to the Colorado River indicating application in rivers without validation data should be carried out with caution. A Python Package named SatTemp (Valman, 2021b) was developed to assist hydropower operators in implementing the method along with a dashboard app to disseminate results (Valman, 2021a). Concerns were raised with the “black box” nature of Google Earth Engine and this App, meaning that errors and nuances in the method may be missed. These would need to be addressed before this method can be provided to hydropower operators.
... Concerns about declines of cold-water fishes have resulted in extensive water temperature monitoring and restoration expenditures globally (Daigle et al., 2016, Jackson et al., 2016, Isaak et al., 2017. Across the western United States, hundreds of millions of dollars have been spent (Barnas et al., 2015). ...
Restoring degraded rivers requires initial assessment of the fluvial landscape to identify stressors and riverine features that can be enhanced. We associated local-scale river habitat data collected using standardized national monitoring tools with modeled regional water temperature and flow data on mid-sized northwest U.S. rivers (30-60 m wide). We grouped these rivers according to quartiles of their modeled mean August water temperature and examined their physical habitat structure and flow. We then used principal components analysis to summarize the variation in several dimensions of physical habitat. We also compared local conditions in the Priest River, a river targeted for restoration of native salmonid habitat in northern Idaho, with those in other rivers of the region to infer potential drivers controlling water temperature. The warmest rivers had physical structure and fluvial characteristics typical of thermally degraded rivers, whereas the coldest rivers had higher mean summer flows and greater channel planform complexity. The Priest River sites had approximately twice as many deep residual pools (>50, >75, and >100 cm) and incision that averaged approximately twice that in the coldest rivers. Percentage fines and natural cover in the Priest were also more typical of the higher-temperature river groups. We found generally low instream cover and low levels of large wood both across the region and within the Priest River. Our approach enabled us to consider the local habitat conditions of a river in the context of other similarly sized rivers in the surrounding region. Understanding this context is important for identifying potential influences on river water temperature within the focal basin and for defining attainable goals for management and restoration of thermal and habitat conditions.
... Moreover, manual monitoring does not allow for same-day measurements of WT at the different stations. Considering its high seasonality and variability, WT monitoring derives substantial benefits from continuous measurements (Daigle, Caudron, Vigier, & Pella, 2017). For instance, continuous monitoring is essential to detect extreme thermal events that, though limited in time, could be harmful to aquatic organisms such as fish (Lessard & Hayes, 2003). ...
Many studies focus on stream water temperature (WT) because it is considered a key ecological factor. However, few of them have investigated the use of WT data from water level monitoring networks, which often measure WT as ancillary data. Our study was conducted in southern Belgium at a high temporal resolution with continuous data recorded at intervals of 10 min between 2012 and 2016 and large spatial scale greater than 16,000 km 2. This study aimed to assess whether a regional water level network (140 stations) is reliable for continuous WT monitoring based on a Bland-Altman analysis with WT collected through a European monitoring network (Water Framework Directive). This study also investigates whether WT data acquired by water level stations can be used to perform both state-of-the-art visualization of thermal regimes and spatio-temporal queries for specific ecological monitoring. We found that the water level stations were reliable tools in recording continuous WT in the streams of the study area. The temperature difference between the two WT monitoring networks was −0.57°C on average. Our positive results promote the use of WT from water level stations in order to globally characterize the thermal regime of streams as well as to provide spatial or temporal information on this regime at high frequencies. As an example, our data showed the effectiveness for brown trout (Salmo trutta fario L.) in spatializing thermal risk areas related to the thermal requirement of this fish species; in 2015, 19% of stations located in brown trout fish zone recorded temperatures above 25°C.
... The advent of inexpensive sensors, combined with regulatory requirements and concerns about climate change, have led to the recent expansion in temperature monitoring networks for rivers and streams Rivers-Moore et al., 2013;Hilderbrand et al., 2014;Luce et al., 2014b;Trumbo et al., 2014;Hannah and Garner, 2015;Jackson et al., 2016;Molinero et al., 2016;Daigle et al., 2016;Mauger et al., 2016;Steel et al., 2016). What was once a data dearth is becoming a deluge and opportunities exist to study thermal regimes with robust data sets. ...
Description of thermal regimes in flowing waters is key to understanding physical processes, enhancing predictive abilities, and improving bioassessments. Spatially and temporally sparse data sets, especially in logistically challenging mountain environments, have limited studies on thermal regimes, but inexpensive sensors coupled with crowd-sourced data collection efforts provide efficient means of developing large data sets for robust analyses. Here, thermal regimes are assessed using annual monitoring records compiled from several natural resource agencies in the northwestern United States that spanned a 5-year period (2011–2015) at 226 sites across several contiguous montane river networks. Regimes were summarized with 28 metrics and principal component analysis (PCA) was used to determine those metrics which best explained thermal variation on a reduced set of orthogonal axes. Four principal components (PC) accounted for 93.4 % of the variation in the temperature metrics, with the first PC (49 % of variance) associated with metrics that represented magnitude and variability and the second PC (29 % of variance) associated with metrics representing the length and intensity of the winter season. Another variant of PCA, T-mode analysis, was applied to daily temperature values and revealed two distinct phases of spatial variability – a homogeneous phase during winter when daily temperatures at all sites were
... The advent of inexpensive sensors, combined with regulatory requirements and concerns about climate change, have led to the recent expansion in temperature monitoring networks for rivers and streams Rivers-Moore et al., 2013;Hilderbrand et al., 2014;Luce et al., 2014a;Trumbo et al., 2014;Hannah and Garner, 2015;Jackson et al., 2016;Molinero et al., 2015;Daigle et al., 2016;Mauger et al., 2016;Steele et al., 2016). What was once a data dearth is becoming a deluge and opportunities exist to study thermal regimes with robust datasets. ...
Description of thermal regimes in flowing waters is key to understanding physical processes, enhancing predictive abilities, and improving bioassessments. Spatially and temporally sparse datasets, especially in logistically challenging mountain environments, have limited studies on thermal regimes but inexpensive sensors coupled with crowd-sourced data collection efforts provide efficient means of developing large datasets for robust analyses. Here, thermal regimes are assessed using annual monitoring records spanning a five-year period (2011–2015) at 226 sites across several contiguous montane river networks in the northwestern U.S. Regimes were summarized with 28 metrics and principle components analysis (PCA) was used to determine those metrics which best explained thermal variation on a reduced set of orthogonal axes. Four principle components (PC) accounted for 93.4 % of the variation in the temperature metrics, with the first PC (49 % of variance) associated with metrics that represented magnitude and variability and the second PC (29 % of variance) associated with metrics representing the length and intensity of the winter season. Another variant of PCA, T-mode analysis, was applied to daily temperature values and revealed two distinct phases of spatial variance – a homogeneous phase during winter when daily temperatures at all sites were
... Thermalscape characterization of river segments has advanced significantly in recent decades through innovations in remote Sensor technologies for measuring stream temperatures have proliferated in recent decades (Dugdale, 2016;Ebersole et al., 2003;Quilty & Moore, 2007;Selker et al., 2006;Torgersen et al., 2012;Vaccaro & Maloy, 2006) but the most popular have been inexpensive sensors that record measurements at user-specified intervals and create time series of recordings (Angilletta & Krochmal, 2003;Dunham et al., 2005;Stamp et al., 2014). Inexpensive sensors democratized the collection of temperature data beginning in the early 1990s, which resulted in extensive, albeit largely uncoordinated, monitoring efforts throughout North America and Europe (Daigle et al., 2016;DeWeber & Wagner, 2014;Dunham et al., 2003;Hannah & Garner, 2015;Hilderbrand et al., 2014;Isaak et al., 2010;Isaak & Hubert, 2001;Jackson et al., 2016;Johnson & Wilby, 2015;Mauger et al., 2016;McKenna et al., 2010;Molinero et al., 2016;Moore et al., 2013;Trumbo et al., 2014;Wehrly et al., 2009). Sensors deployed in those efforts sometimes record data only for short periods (e.g., 1-3 months or years) but viewed collectively, constitute a massive distributed monitoring array that provides measurements from thousands of sites. ...
Thermal regimes are fundamental determinants of aquatic ecosystems, which makes description and prediction of temperatures critical during a period of rapid global change. The advent of inexpensive temperature sensors dramatically increased monitoring in recent decades, and although most monitoring is done by individuals for agency-specific purposes, collectively these efforts constitute a massive distributed sensing array that generates an untapped wealth of data. Using the framework provided by the National Hydrography Dataset, we organized temperature records from dozens of agencies in the western U.S. to create the NorWeST database that hosts >220,000,000 temperature recordings from >22,700 stream and river sites. Spatial-stream-network models were fit to a subset of those data that described mean August water temperatures (AugTw) during 63,641 monitoring site-years to develop accurate temperature models (r2 = 0.91; RMSPE = 1.10 ᵒC; MAPE = 0.72 ᵒC), assess covariate effects, and make predictions at 1-km intervals to create summer climate scenarios. AugTw averaged 14.2 ᵒC (SD = 4.0 ᵒC) during the baseline period of 1993-2011 in 343,000 km of western perennial streams but trend reconstructions also indicated warming had occurred at the rate of 0.17 ᵒC/decade (SD = 0.067 ᵒC/decade) during the 40-year period of 1976-2015. Future scenarios suggest continued warming, although variation will occur within and among river networks due to differences in local climate forcing and stream responsiveness. NorWeST scenarios and data are available online in user-friendly digital formats and are widely used to coordinate monitoring efforts among agencies, for new research, and for conservation planning.
... Spatial statistical Tw models have been facilitated by recent technological and statistical developments. These include 1) increased Tw data availability (Isaak et al., 2011;Jackson et al., 2016), 2) improved monitoring design (Dobbie et al., 2008;Som et al., 2014;Jackson et al., 2016;Daigle et al., 2017), 3) greater availability of spatial data e.g. "National Stream Internet" (Nagel et al., 2016), 4) improvements in spatial analysis software across different platforms e.g. ...
The thermal suitability of riverine habitats for cold water adapted species may be reduced under climate change. Riparian tree planting is a practical climate change mitigation measure, but it is often unclear where to focus effort for maximum benefit. Recent developments in data collection, monitoring and statistical methods have facilitated the development of increasingly sophisticated river temperature models capable of predicting spatial variability at large scales appropriate to management. In parallel, improvements in temporal river temperature models have increased the accuracy of temperature predictions at individual sites. This study developed a novel large scale spatio-temporal model of maximum daily river temperature (Twmax) for Scotland that predicts variability in both river temperature and climate sensitivity. Twmax was modelled as a linear function of maximum daily air temperature (Tamax), with the slope and intercept allowed to vary as a smooth function of day of the year (DoY) and further modified by landscape covariates including elevation, channel orientation and riparian woodland. Spatial correlation in Twmax was modelled at two scales; (1) river network (2) regional. Temporal correlation was addressed through an autoregressive (AR1) error structure for observations within sites. Additional site level variability was modelled with random effects. The resulting model was used to map (1) spatial variability in predicted Twmax under current (but extreme) climate conditions (2) the sensitivity of rivers to climate variability and (3) the effects of riparian tree planting. These visualisations provide innovative tools for informing fisheries and land-use management under current and future climate.
The availability of Québec (Canada) river temperature data has improved significantly in recent years, with increased monitoring effort and sharing of existing data. Although it is now recognized that this variable is of central interest in a variety of instream habitat issues, the actual interpretation and use of the data by managers and researchers can be challenging, due to the spatial sparsity of the measurement sites and to the unevenness of the length of the temperature time series. In this paper, we propose a model for all Québec rivers thermal regimes, as a means to interpret and exploit the available river temperature data. The model, based on fitting a Gaussian function to time series of interannual mean daily temperatures, was used to characterize the thermal regime at more than 400 locations in Québec rivers. The three parameters of the model (annual maximum, date of annual maximum and length of the warm season) allowed to produce a first map of Québec river thermal regimes. A method to quantify the uncertainty on the parameters estimation, taking into account the length of the time series and the interannual variability of the river temperature, is presented. Finally, stepwise regressions were used to investigate the relationships between the model parameters and the physioclimatic characteristics of the basins upstream of the measurement sites.
The acquisition of good hydrologic information is an important issue in water management since it is the basis of decisions concerning the allocation of water resources to different users. However, sufficient data are often not available to describe the behaviour of such systems, especially in developing countries, where monitoring networks are inappropriately designed, poorly operated or are inadequate. Therefore, it is of interest to design and evaluate efficient monitoring networks. This paper presents two methodologies to design discharge monitoring networks in rivers using the concepts of Information Theory. The first methodology considers the optimization of Information Theory quantities and the second considers a new method that is based on ranking Information Theory quantities with different possible monitor combinations. The methodologies are tested for the Magdalena River in Colombia, in which the existing monitoring network is also assessed. In addition, the use of monitors at tributaries is explored. It is demonstrated that the ranking method is a promising way of finding the extremes of Pareto fronts generated during multi-objective optimization processes and that better (more informative and less redundant) monitoring network configurations can be found for the Magdalena River.
The effects of industrial cooling water on fish communities
were widely debated at the end of the seventies, when large thermal
power plants were being developed. This led to numerous research programs
on thermal tolerance in fish. The recent warming climatic period
and especially the 2003 heat wave have brought thermal biology back
to center stage. The work presented here has consisted in analyzing
historical and contemporary literature to update basic knowledge
on thermal tolerances of 19 riverine fish species. These data were
then validated and completed by European fish specialists. We finally proposed
a synthesis, based on more than 300 references, which details thermal
tolerances for the entire life cycle: reproduction, embryonic, larval,
juvenile and adult life stages. This updated material is of great importance
to an understanding of the trends observed in fish communities or
for forecasting future behavior in climate change scenarios.
Change in river water temperature has important consequences for the environment and people. This review provides a new perspective on the topic by evaluating changes in river water temperature for the UK over the 20th century and possible changes over the 21st century. There is limited knowledge of space-time variability in, and controls on, river temperature at the region scale and beyond over the 20th century. There is historical evidence that UK river temperature has increased in the latter part of the 20th century, but low agreement on the attribution of changes to climatic warming because river temperature is a complex, dynamic response to climate and hydrological patterns moderated by basin properties and anthropogenic impacts. Literature is scarce to evaluate changes to UK river temperature in the 21st century, but it appears as likely as not that UK river temperature will increase in the future. However, there are a number of interlinked sources of uncertainty (related to observations, scenarios, process interactions and feedback) that make estimating direction and rate of temperature change for rivers across the UK with confidence very challenging. Priority knowledge gaps are identified that must be addressed to improve understanding of past, contemporary and future river temperature change.
The temperature of stream water is an important control of many in-stream processes. To better understand the processes and consequences of solar energy inputs to streams, stream temperature dynamics were examined before, during, and after experimental shading of a 150-m reach of a second-order stream in the Oregon Cascade Range. Maximum water temperatures declined significantly in the shaded reach, but minimum and mean temperatures were not modified. Heat budget calculations before shading show the dominance of solar energy as an influence of stream tem-perature. The influence of substrate type on stream temperature was examined separately where the water flowed first over bedrock and then through alluvial substrates. Maximum temperatures in the upstream bedrock reach were up to 8.6 °C higher and 3.4 °C lower than downstream in the alluvial reach. Better understanding of factors that influence not only maximum but minimum temperatures as well as diurnal temperature variation will highlight types of reaches in which stream temperature would be most responsive to changes in shading. Many apparent discrepancies in stream temperature literature can be explained by considering variation in the relative importance of different stream temperature drivers within and among streams and over time.
The effects of climate warming on the thermal habitat of 57 species of fish of the U.S. were estimated using results for a doubling of atmospheric carbon dioxide that were predicted by the Canadian Climate Center general circulation model. Baseline water temperature conditions were calculated from data collected at 1,700 U.S. Geological Survey stream monitoring stations across the U.S. Water temperatures after predicted climate change were obtained by multiplying air temperature changes by 0.9, a factor based on several field studies, and adding them to baseline water temperatures at stations in corresponding grid cells. Results indicated that habitat for cold and cool water fish would be reduced by ~50%, and that this effect would be distributed throughout the existing range of these species. Habitat losses were greater among species with smaller initial distributions and in geographic regions with the greatest warming (e.g. the central Midwest). Results for warm water fish habitat were less certain because of the poor state of knowledge regarding their high and low temperature tolerances; however, the habitat of many species of this thermal guild likely will also be substantially reduced by climate warming, whereas the habitat of other species will be increased.
A physiographical space-based kriging method is proposed for regional flood frequency estimation. The methodology relies on the construction of a continuous physiographical space using physiographical and meteorological characteristics of gauging stations and the use of multivariate analysis techniques. Two multivariate analysis methods were tested: canonical correlation analysis (CCA) and principal components analysis. Ordinary kriging, a geostatistical technique, was then used to interpolate flow quantiles through the physiographical space. Data from 151 gauging stations across the southern part of the province of Quebec, Canada, were used to illustrate this approach. In order to evaluate the performance of the proposed method, two validation techniques, cross validation and split-sample validation, were applied to estimate flood quantiles corresponding to the 10, 50, and 100 year return periods. Results of the proposed method were compared to those produced by a traditional regional estimation method using the canonical correlation analysis. The proposed method yielded satisfactory results. It allowed, for instance, for estimating the 10 year return period specific flow with a coefficient of determination of up to 0.78. However, this performance decreases with the increase in the quantile return period. Results also showed that the proposed method works better when the physiographical space is defined using canonical correlation analysis. It is shown that kriging in the CCA physiographical space yields results as precise as the traditional estimation method, with a fraction of the effort and the computation time.
A characterization of the low-flow regimes of 175 eastern Canadian rivers based on multivariate analysis of hydrological indices (HIs) is presented. Principal component analysis (PCA) was used to identify eight highly informative and low-correlated HIs amongst 67 low-flow HIs reported in the literature, and to test their ability to describe regional characteristics and differences among low-flow regimes at the 175 stations. It was found that eight HIs can provide a regional description of the main low-flow characteristics almost equivalent to using all HIs related to low flows. The PCA also identified regional similarities and differences among the geographical and hydrological regions within the Province of Quebec, as well as between the Atlantic provinces of Canada. Multivariate analysis proved to be an efficient tool that can complement expert knowledge in the selection of criteria for instream flow assessments in eastern Canada, by quantitatively indicating indices carrying high information, and by ensuring low redundancy in the selected subset of variables (HIs).Citation Daigle, A.,St-Hilaire, A., Beveridge, D., Caissie, D. & Benyahya, L. (2011) Multivariate analysis of the low-flow regimes in eastern Canadian rivers. Hydrol. Sci. J.56(1), 51–67.
The utility of hydrologic indices for describing various aspects of streamflow regimes has resulted in their increased application in riverine research. Consequently, researchers are now confronted with the task of having to choose among a large number of competing hydrologic indices to reduce computational effort and variable redundancy prior to statistical analyses, while still adequately representing the major facets of the flow regime. The present study addresses this concern by providing a comprehensive review of 171 currently available hydrologic indices (including the commonly used Indicators of Hydrologic Alteration) using long-term flow records from 420 sites from across the continental USA. We highlight patterns of redundancy among these hydrologic indices and provide a number of statistically and ecologically based recommendations for the selection of a reduced set of indices that can simultaneously (1) explain a dominant proportion of statistical variation in the complete set of hydrologic indices and (2) minimize multicollinearity while still adequately representing recognized, critical attributes of the flow regime. In addition, we examine the transferability of hydrologic indices across ‘stream types’ by identifying indices that consistently explain dominant patterns of variance across streams in varying climatic and geologic environments. Together, our results provide a framework from which researchers can identify hydrologic indices that adequately characterize flow regimes in a non-redundant manner. In combination with ecological knowledge, this framework can guide researchers in the parsimonious selection of hydrologic indices for future hydroecological studies. Copyright © 2003 John Wiley & Sons, Ltd.
Thermal regimes in rivers and streams are fundamentally important to aquatic ecosystems and are expected to change in response
to climate forcing as the Earth’s temperature warms. Description and attribution of stream temperature changes are key to
understanding how these ecosystems may be affected by climate change, but difficult given the rarity of long-term monitoring
data. We assembled 18 temperature time-series from sites on regulated and unregulated streams in the northwest U.S. to describe
historical trends from 1980–2009 and assess thermal consistency between these stream categories. Statistically significant
temperature trends were detected across seven sites on unregulated streams during all seasons of the year, with a cooling
trend apparent during the spring and warming trends during the summer, fall, and winter. The amount of warming more than compensated
for spring cooling to cause a net temperature increase, and rates of warming were highest during the summer (raw trend = 0.17°C/decade;
reconstructed trend = 0.22°C/decade). Air temperature was the dominant factor explaining long-term stream temperature trends
(82–94% of trends) and inter-annual variability (48–86% of variability), except during the summer when discharge accounted
for approximately half (52%) of the inter-annual variation in stream temperatures. Seasonal temperature trends at eleven sites
on regulated streams were qualitatively similar to those at unregulated sites if two sites managed to reduce summer and fall
temperatures were excluded from the analysis. However, these trends were never statistically significant due to greater variation
among sites that resulted from local water management policies and effects of upstream reservoirs. Despite serious deficiencies
in the stream temperature monitoring record, our results suggest many streams in the northwest U.S. are exhibiting a regionally
coherent response to climate forcing. More extensive monitoring efforts are needed as are techniques for short-term sensitivity
analysis and reconstructing historical temperature trends so that spatial and temporal patterns of warming can be better understood.
Continuation of warming trends this century will increasingly stress important regional salmon and trout resources and hamper
efforts to recover these species, so comprehensive vulnerability assessments are needed to provide strategic frameworks for
prioritizing conservation efforts.
This study evaluates the sensitivity of Washington State’s freshwater habitat of Pacific Salmon (Oncorhynchus spp.) to climate change. Our analysis focuses on summertime stream temperatures, seasonal low flows, and changes in peak
and base flows because these physical factors are likely to be key pressure points for many of Washington’s salmon populations.
Weekly summertime water temperatures and extreme daily high and low streamflows are evaluated under multimodel composites
for A1B and B1 greenhouse gas emissions scenarios. Simulations predict rising water temperatures will thermally stress salmon
throughout Washington’s watersheds, becoming increasingly severe later in the twenty-first century. Streamflow simulations
predict that basins strongly influenced by transient runoff (a mix of direct runoff from cool-season rainfall and springtime
snowmelt) are most sensitive to climate change. By the 2080s, hydrologic simulations predict a complete loss of Washington’s
snowmelt dominant basins, and only about ten transient basins remaining in the north Cascades. Historically transient runoff
watersheds will shift towards rainfall dominant behavior, undergoing more severe summer low flow periods and more frequent
days with intense winter flooding. While cool-season stream temperature changes and impacts on salmon are not assessed in
this study, it is possible that climate-induced warming in winter and spring will benefit parts of the freshwater life-cycle
of some salmon populations enough to increase their reproductive success (or overall fitness). However, the combined effects
of warming summertime stream temperatures and altered streamflows will likely reduce the reproductive success for many Washington
salmon populations, with impacts varying for different life history-types and watershed-types. Diminishing streamflows and
higher stream temperatures in summer will be stressful for stream-type salmon populations that have freshwater rearing periods
in summer. Increased winter flooding in transient runoff watersheds will likely reduce the egg-to-fry survival rates for ocean-type
and stream-type salmon.
La reproduction de la truite commune (Salmo trutta L.) a été suivie trois années consécutives
du début novembre à la mi-janvier, dans un petit affluent du Lac Léman. Le suivi a été réalisé en
utilisant deux techniques complémentaires : la pêche électrique et le comptage des frayères.
L'activité de fraie des truites lacustres est comparée à celle des truites sédentaires. L'étude précise
les caractéristiques des populations en place avant la fraie, ainsi que celles des géniteurs de truite de
lac. Les mouvements des juvéniles sont sommairement étudiés.
Studies of physiological mechanisms are needed to predict climate effects on ecosystems at species and community levels.
Spot stream temperatures recorded by Water Survey of Canada technicians during visits to gauging stations in British Columbia were used to characterize stream temperature patterns. A general linear modelling approach was used to relate the median water temperatures for each month to a set of variables describing catchment and climatic characteristics. Models were fitted for stations with drainage areas greater than 100 km 2 to avoid possible regional bias, because most of the stations with areas less than 100 km 2 were located south of 52°N. The final predictor variables varied by month and, for July and August, included normal monthly maximum air temperature, normal annual precipitation, mean catchment elevation, logarithm of drainage area, percent glacier cover, percent lake cover, and whether or not the flow regime is regulated. The models explained up to 79% of the variance in water temperature, with standard errors of the estimate ranging from 0.7°C in January to 1.6°C in August. Values of the coefficients for the predictor variables and their seasonal variations were consistent with the physical processes governing stream temperature. These models suggest that catchment characteristics leave an imprint on stream temperature, even for large catchments, and need to be accounted for in efforts to model regional variations in stream temperature.
The theoretical hydrographical network (RHT) is a new digital hydrographical network derived from the BD Alti® digital elevation model of the French Geographic National Institute (IGN). We used the “Agree” method to build the RHT network. This method modifies the topography of the digital elevation model to make it consistent with a vector coverage corresponding to the observed stream network. Specifically, the RHT is developed from the BD Alti® modified by the RHE network, the latter being a joined and oriented simplification of the observed hydrographical network of the IGN, BD Carthage®. The consistency between the RHT and the BD Alti®enables a precise identification of catchments and improved simulations of flows. This approach enables the integration of several spatial attributes and their accumulation along the network. We calculated various topologic, hydrologic and climatic attributes for the RHT and integrated them in a geographic information system. Comparisons and tests have been made to evaluate the new hydrographical network and its environmental attributes. The network has many potential applications associated with integrated basin management.
Hydrometric information constitutes the fundamental input for planning,
design, operation, and management of water resources systems. How to
optimally site monitoring gauges such that they are effective and
efficient in gathering the hydrometric information or data has received
considerable attention. This paper presents a generic approach for the
design (or evaluation) of hydrometric networks. First, an entropy
theory-based criterion, named as maximum information minimum redundancy
(MIMR), is proposed. The MIMR criterion maximizes the joint entropy of
stations within the optimal set, and the transinformation between
stations within and outside of the optimal set. Meanwhile, it insures
that the optimal set contains minimum duplicated information. An
easy-to-implement greedy ranking algorithm is developed to accomplish
the MIMR selection. Two case studies are presented to illustrate the
applicability of MIMR in hydrometric network evaluation and design. We
also compare the MIMR selection with another entropy-based approach.
Results illustrate that MIMR is apt at finding stations with high
information content, and locating independent stations. The proposed
approach is suitable for design (or evaluation) of any type of
hydrometric network.
Existing methods of hydrologic network design are reviewed and a formulation based on C. E. Shannon's information theory is presented. This type of formulation involves the computation of joint entropy terms which can be computed by discretizing hydrologic time series data collected at station locations. The computation of discrete entropy terms is straightforward but in handling large numbers of stations enormous computation time and storage is required. In order to minimize these problems, bivariate and multivariate continuous distributions are used to derive entropy terms. The information transmission at bivariate level is derived for normal, lognormal, gamma, exponential, and extreme value distributions. At the multivariate level, multivariate form or normal and lognormal probability density functions are used. The applicability of the derived information relationship is illustrated.
This review attempts to assess, as quantitatively as possible, the habitat requirements in fresh water of three common and widespread European salmonid species. Namely: the trout (Salmo trutta L.), the salmon (S. salar L.) and the grayling (Thymallus thymallus L.). Requirements are considered for spawning, incubation and emergence, juveniles and smolts and for adults and spawning movements.
The objective of this study was to predict interannual fluctuations in the emergence period of sea trout fry, using models developed from field data for 70 excavated redds, and laboratory data on egg and alevin development at 30 constant temperatures (range 1·5–10·5° C with 100 naturally fertilized eggs at each temperature). Egg weight and numbers per redd both increased with female length; a power function described the relationship. Early spawners were the largest females laying the largest and most numerous eggs, whilst late spawners were the smallest females laying the smallest and least numerous eggs; middle spawners being intermediate between these two extremes. Mean values for egg weight and numbers of eggs per redd were obtained for these three groups. Hatching and emergence times in the laboratory decreased with increasing temperature. Of five models tested for hatching time, the best fit was provided by a three-parameter hyperbolic model which formed the asis of the individual-based model used to predict egg hatching and fry emergence. Model development was described in detail and the final equations predicted the times taken for 5, 50 and 95% of the fry to emerge, and hence the period over which 90% of the fry emerged. Analogous models were obtained for egg hatching. All models were excellent fits to the laboratory data. Hatching times for eggs kept in perforated boxes in the stream were almost identical to those kept at similar mean temperatures in the laboratory. Model predictions of fry emergence times were validated by field data for 8 years (1967–1971, 1974, 1975, 1980). The chief objective was therefore fulfilled, and predictions for the 30-year study (1967–1996) revealed a large variation in the timing of emergence (extremes: 11 March–4 April 1989, 15–20 May 1979). Most of the variation in median emergence date was due to variations in water temperature, with spawning dates as a secondary factor; the latter, however, had a greater effect on the length of the emergence period.
1. The literature contains a number of curves relating time (days) required for median hatch (=D2) to water temperature (T,°C) for the eggs of several salmonid fishes. There are relatively few data on the relationships between time to median eyed (= D1), days) or time to median swim-up (= D3, days) and temperature.
2. From published data, over most of the range 0-9.5°C, approximate relationships are D,=0.5D2 for Atlantic salmon (Salmo salar L.) and D3 = 1.7D2 for eight species of Salmo and Oncorhynchus.
3. Field and hatchery tests suggest that these are useful empirical models for approximate prediction of D1 and D3 from D2 for most salmonids.
The lack of geographically broad-scale temperature data has limited our ability to classify stream temperatures and assess the processes affecting them. Continuous data (1 July 2005–30 June 2006) from 90 sites throughout the Great Lakes Basin (GLB) were used to classify and model the thermal regimes of streams in Ontario. Existing and newly developed temperature metrics were used to characterize the data for each site. The 90 sites clustered into three thermal regimes based on maximum weekly maximum temperature (°C) and spring rate of change (°C · d−1). The centroids of regime 1, 2 and 3 had temperatures of 26.4, 28.4, 23.5°C and warming rates of 0.20, 0.12 and 0.10°C · d−1, respectively. There was a regional pattern in the thermal regimes; most sites in the north were regime 1 and most sites in the south were regime 2 but neither regime was limited to those areas. Regime 3 sites were found throughout the study area. Discriminant function analysis indicated that per cent riparian forest, mean annual air temperature, per cent surface water and groundwater discharge potential influenced the thermal regimes at the sites, and demonstrated how variables at three spatial scales influence stream temperatures. This study provides a framework for thermal assessments elsewhere and demonstrates how anthropogenic activities such as riparian deforestation, groundwater withdrawal, stream regulation and climate change will all affect the main drivers of thermal regimes in streams. Copyright © 2009 John Wiley & Sons, Ltd.
Proliferative kidney disease (PKD) is caused by the infection of susceptible salmonid fish with spores of the myxozoan Tetracapsula bryosalmonae, a parasite harboured and released by several species of bryozoans. Under natural conditions, PKD is a water-borne infection of fish, whose outcome and spatio-temporal dissemination depend on the viability of spores present in the water. In order to evaluate the duration of parasite infectivity, juvenile rainbow trout, Oncorhynchus mykiss, were exposed for 20 h to T. bryosalmonae-infected water at various times post-water collection or after different filtration procedures. When infected water was held in a temperature range of 14.5–17 °C for up to 14 days, PKD was transmitted to the fish only between 0 and 12 h post-water collection and its infectivity vanished between 12 and 24 h. Similarly, the infectivity of water passed through 25 μm but not through 1 μm mesh filters, and was lost in the material eluted from the 1 μm filtration membrane although the parasite's DNA was amplified from this material. The parasitic infectivity in water appears to be fragile and this may offer opportunities to decrease the impact of PKD in trout farms by the implementation of management procedures aimed at reducing the number of the bryozoan-holding surfaces located in the river, immediately upstream from these farms.
Summary1. The thermal regime of rivers plays an important role in the overall health of aquatic ecosystems, including water quality issues and the distribution of aquatic species within the river environment. Consequently, for conducting environmental impact assessments as well as for effective fisheries management, it is important to understand the thermal behaviour of rivers and related heat exchange processes.2. This study reviews the different river thermal processes responsible for water temperature variability on both the temporal (e.g. diel, daily, seasonal) and spatial scales, as well as providing information related to different water temperature models currently found in the literature.3. Water temperature models are generally classified into three groups: regression, stochastic and deterministic models. Deterministic models employ an energy budget approach to predict river water temperature, whereas regression and stochastic models generally rely on air to water temperature relationships.4. Water temperature variability can occur naturally or as a result of anthropogenic perturbations, such as thermal pollution, deforestation, flow modification and climate change. Literature information is provided on the thermal regime of rivers in relation to anthropogenic impacts and such information will contribute to the better protection of fish habitat and more efficient fisheries management.
To project potential habitat changes of 57 fish species under global warming, their suitable thermal habitat at 764 stream gaging stations in the contiguous United States was studied. Global warming was specified by air temperature increases projected by the Canadian Centre of Climate Modelling General Circulation Model for a doubling of atmospheric CO2. The aquatic thermal regime at each gaging station was related to air temperature using a nonlinear stream temperature/air temperature relationship.Suitable fish thermal habitat was assumed to be constrained by both maximum temperature and minimum temperature tolerances. For cold water fishes with a 0 C lower temperature constraint, the number of stations with suitable thermal habitat under a 2CO2 climate scenario is projected to decrease by 36%, and for cool water fishes by 15%. These changes are associated with a northward shift of the range. For warm water fishes with a 2 C lower temperature constraint, the potential number of stations with suitable thermal habitat is projected to increase by 31%.
The onset date of positive water temperature in the annual thermal cycle of North-American streams is modeled using parametric (regression) and non-parametric (artificial neural networks) approaches. Physiographic, land cover and weather-related variables are used to predict the date of positive temperature onset for 191 station-years at 48 locations in Canada and in Northern US. Preliminary correlation analysis is performed in order to test the relationships between the physiographic/land cover/weather variables and the date of positive temperature onset. Moreover, several different subsets of variables are tested as inputs to each model type. Artificial neural networks can predict the date of positive temperature onset for a given station-year, given its longitude, lake coverage of its drainage basin, and two January–February daily temperature indices, with a split-sample validation root mean square error (RMSE) ∼8.8 days. Ordinary least square (OLS) regression models allow to predict the onset date with RMSE ∼9.5 days, given the station’s latitude, longitude, lake coverage and one January–February daily temperature index. OLS regression models adjusted on canonical variates combining 13 physiographic/land cover and weather variables achieve prediction performance ∼9.1 days. The precipitation does not impact much on the onset date prediction for all tested models.
There are several deficiencies in the statistical approaches proposed in the literature for the assessment and redesign of surface water-quality-monitoring locations. These deficiencies vary from one approach to another, but generally include: (i) ignoring the attributes of the basin being monitored; (ii) handling multivariate water quality data sequentially rather than simultaneously; (iii) focusing mainly on locations to be discontinued; and (iv) ignoring the reconstitution of information at discontinued locations. In this paper, a methodology that overcomes these deficiencies is proposed. In the proposed methodology, the basin being monitored is divided into sub-basins, and a hybrid-cluster analysis is employed to identify groups of sub-basins with similar attributes. A stratified optimum sampling strategy is then employed to identify the optimum number of monitoring locations at each of the sub-basin groups. An aggregate information index is employed to identify the optimal combination of locations to be discontinued. The proposed approach is applied for the assessment and redesign of the Nile Delta drainage water quality monitoring locations in Egypt. Results indicate that the proposed methodology allows the identification of (i) the optimal combination of locations to be discontinued, (ii) the locations to be continuously measured and (iii) the sub-basins where monitoring locations should be added. To reconstitute information about the water quality variables at discontinued locations, regression, artificial neural network (ANN) and maintenance of variance extension (MOVE) techniques are employed. The MOVE record extension technique is shown to result in a better performance than regression or ANN for the estimation of information about water quality variables at discontinued locations.
La reproduction d’une population de truite (Salmo trutta L.) de forme essentiellement méditerranéenne est décrite dans le ruisseau du Chevenne, un torrent à forte pente (10 %) entrecoupé d’obstacles et limité en substrat de frai, situé dans les Alpes savoyardes. Les reproducteurs (résidents du torrent ou migrants issus du cours principal) diffèrent en taille et sexe-ratio. La majorité (58 %) des femelles migrantes entrent dans l’affluent déjà ovulées. Il existe une fort gradient décroissant aval-amont dans la répartition des géniteurs et des frayères. Les 157 frayères décrites montrent une grande diversité de microhabitats (5 types principaux) utilisés pour la reproduction, avec un pourcentage élevé (65 %) de frayères construites dans des sites protégés ayant une faible vitesse de courant (< 20 cm/s). La reproduction débute en radier, l’habitat de frai classique, puis elle s’étend ensuite aux autres microhabitats avant que le frai en milieu radier ne soit achevé. Une évaluation de la disponibilité et de l’utilisation du substrat favorable au frai (taille de 1 à 3 cm) dans les divers microhabitats a été réalisée a posteriori. Le microhabitat « côté de pool » (inhabituel pour le frai) est autant utilisé que le milieu radier. Les lentilles de substrat favorable, abritées en bordure de berge ou dans les courants sont préférées alors que les lentilles de substrat non protégées dans les courants sont évitées. Une crue en fin de la période de frai a totalement détruit 36 % des frayères avec un taux de destruction totale très variable selon le type de microhabitat de frai. Les frayères creusées en côté de pool et en bordure de berge protégées du courant ont été moins détruites (15-17 % de destruction totale) que dans les autres microhabitats (50 à 67 % de destruction totale). La diversité des sites de frai pourrait donc être une composante essentielle à la survie de la population de truite en milieu « torrent exposé à des crues hivernales ».
An up-to-date review of the statistical approaches utilized for the assessment and redesign of surface water quality monitoring (WQM) networks is presented. The main technical aspects of network design are covered in four sections, addressing monitoring objectives, water quality variables, sampling frequency and spatial distribution of sampling locations. This paper discusses various monitoring objectives and related procedures used for the assessment and redesign of long-term surface WQM networks. The appropriateness of each approach for the design, contraction or expansion of monitoring networks is also discussed. For each statistical approach, its advantages and disadvantages are examined from a network design perspective. Possible methods to overcome disadvantages and deficiencies in the statistical approaches that are currently in use are recommended.
La méthode présentée brièvement ici a été conçue à la subdivision d'Hydrométéorologie de la Météorologie Nationale pour répondre de façon automatique et opérationnelle à des besoins en cartographie de paramètres pluviométriques statistiques. Après définition de la notion de "paysage" environnant un point de mesure, on expose une méthode de codage et de reconnaissance automatique du "paysage" sur un domaine géographique déterminé. On développe ensuite une technique d'analyse permettant la cartographie d'un champ la plus réaliste possible, puisque prenant en compte en chaque point de mesure la valeur observée et le "paysage" associé. Cette technique, baptisée AURELHY, est appliquée à des champs pluviométriques statistiques sur n'importe quelle région de France. L'exemple du Massif Central est traité. L'exposé complet de la méthode ainsi qu'une bibliographie détaillée font l'objet de l'article cité en référence. (Résumé d'auteur)
The principal instrument to temporally and spatially manage water resources is a water quality monitoring network. However, to date in most cases, there is a clear absence of a concise strategy or methodology for designing monitoring networks, especially when deciding upon the placement of sampling stations. Since water quality monitoring networks can be quite costly, it is very important to properly design the monitoring network so that maximum information extraction can be accomplished, which in turn is vital when informing decision-makers. This paper presents the development of a methodology for identifying the critical sampling locations within a watershed. Hence, it embodies the spatial component in the design of a water quality monitoring network by designating the critical stream locations that should ideally be sampled. For illustration purposes, the methodology focuses on a single contaminant, namely total phosphorus, and is applicable to small, upland, predominantly agricultural-forested watersheds. It takes a number of hydrologic, topographic, soils, vegetative, and land use factors into account. In addition, it includes an economic as well as logistical component in order to approximate the number of sampling points required for a given budget and to only consider the logistically accessible stream reaches in the analysis, respectively. The methodology utilizes a geographic information system (GIS), hydrologic simulation model, and fuzzy logic.
The evolutionary history of brown trout (Salmo trutta L.) inferred from phylogeographic, nested clade, and mismatch analyses of mitochondrial DNA variation
- L Bernatchez
Bernatchez, L., 2001. The evolutionary history of brown trout
(Salmo trutta L.) inferred from phylogeographic, nested
clade, and mismatch analyses of mitochondrial DNA variation. Evolution, 55, 351-379. doi:10.1111/evo.2001.55.
issue-2
Spatial heterogeneity of water temperature across an alpine river basin
- L E Brown
- D M Hannah
Brown, L.E. and Hannah, D.M., 2008. Spatial heterogeneity
of water temperature across an alpine river basin.
Hydrological Processes, 22, 954-967. doi:10.1002/(ISSN)
1099-1085
Prediction, from water temperature, of eyeing, hatching and "swim-up" times for salmonids embryos Measurement of stream water temperature and biologiacal applications to salmonid fishes, grayling and dace
- D T Crisp
Crisp, D.T., 1988. Prediction, from water temperature, of
eyeing, hatching and "swim-up" times for salmonids
embryos. Freshwater Biology, 19, 41-48. doi:10.1111/
j.1365-2427.1988.tb00325.x
Crisp, D.T., 1992. Measurement of stream water temperature
and biologiacal applications to salmonid fishes, grayling
and dace. Ambleside: Freshwater Biological Association.
Temperature requirements of Atlantic salmon Salmo salar, brown trout Salmo trutta and Arctic charr Salvelinus alpinus: predicting the effects of climate change
- J M Elliott
- J A Elliott
Elliott, J.M. and Elliott, J.A., 2010. Temperature requirements
of Atlantic salmon Salmo salar, brown trout Salmo trutta
and Arctic charr Salvelinus alpinus: predicting the effects
of climate change. Journal of Fish Biology, 77, 1793-1817.
doi:10.1111/jfb.2010.77.issue-8
Implications of climate change for the fishes of the British Isles
- C T Graham
- C Harrod
Graham, C.T. and Harrod, C., 2009. Implications of climate
change for the fishes of the British Isles. Journal of Fish
Biology, 74, 1143-1205. doi:10.1111/jfb.2009.74.issue-6
Statistical tools for thermal regime characterization at segment river scale: case study of the Ste-Marguerite River. River Research and Applications
- N Guillemette
Guillemette, N., et al., 2011. Statistical tools for thermal
regime characterization at segment river scale: case study
of the Ste-Marguerite River. River Research and
Applications, 27, 1058-1071. doi:10.1002/rra.v27.8
Consequences of climatic change for water temperature and brown trout populations in Alpine rivers and streams
- R E Hari
Hari, R.E., et al., 2006. Consequences of climatic change for
water temperature and brown trout populations in Alpine
rivers and streams. Global Change Biology, 12, 10-26.
doi:10.1111/gcb.2006.12.issue-1
Thermal regimes in a large upland salmon river: a simple model to identify the influence of landscape controls and climate change on maximum temperatures
- M Hrachowitz
Hrachowitz, M., et al., 2010. Thermal regimes in a large
upland salmon river: a simple model to identify the influence of landscape controls and climate change on maximum temperatures. Hydrological Processes, 24, 33743391. doi:10.1002/hyp.v24:23
Effects of climate change and wildfire on stream temperatures and salmonid thermal habitat in a mountain river network
- D J Isaak
Isaak, D.J., et al., 2010. Effects of climate change and wildfire
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