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1.
Daily stream temperatures are needed in a number of analyses. Such analyses might focus on aquatic organisms or industrial activities. To protect aquatic systems, industrial activities, for example, water withdrawals or discharges, are sometimes restricted. To evaluate where new industrial settings should be placed or if climate change will affect already existing industrial settings, the simulation of stream temperature is needed. Stream temperature models with weekly or monthly time scale might not be sufficient for this kind of analysis. Different regression models to simulate daily stream temperature for the river Elbe (Germany) are developed and their performance is estimated. For the calibration period the Nash–Sutcliffe coefficient (NSC) for the simplest model is 0·97, and the root mean squared error (RMSE) is 1·48 °C. For the most sophisticated model the NSC also is 0·97. However, the RMSE is 1·32 °C. For the validation period the NSC for the simplest model is 0·96, and the RMSE is 1·45 °C. The NSC for the most sophisticated model is 0·97, and the RMSE is 1·25 °C. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
2.
River temperature models play an increasingly important role in the management of fisheries and aquatic resources. Among river temperature models, forecasting models remain relatively unused compared to water temperature simulation models. However, water temperature forecasting is extremely important for in-season management of fisheries, especially when short-term forecasts (a few days) are required. In this study, forecast and simulation models were applied to the Little Southwest Miramichi River (New Brunswick, Canada), where water temperatures can regularly exceed 25–29°C during summer, necessitating associated fisheries closures. Second- and third-order autoregressive models (AR2, AR3) were calibrated and validated using air temperature as the exogenous variable to predict minimum, mean and maximum daily water temperatures. These models were then used to predict river temperatures in forecast mode (1-, 2- and 3-day forecasts using real-time data) and in simulation mode (using only air temperature as input). The results showed that the models performed better when used to forecast rather than simulate water temperatures. The AR3 model slightly outperformed the AR2 in the forecasting mode, with root mean square errors (RMSE) generally between 0.87°C and 1.58°C. However, in the simulation mode, the AR2 slightly outperformed the AR3 model (1.25°C < RMSE < 1.90°C). One-day forecast models performed the best (RMSE ~ 1°C) and model performance decreased as time lag increased (RMSE close to 1.5°C after 3 days). The study showed that marked improvement in the modelling can be accomplished using forecasting models compared to water temperature simulations, especially for short-term forecasts.
EDITOR M.C. Acreman ASSOCIATE EDITOR S. Huang 相似文献
3.
Behrouz Ahmadi‐Nedushan André St‐Hilaire Taha B. M. J. Ouarda Laurent Bilodeau Élaine Robichaud Nathalie Thiémonge Bernard Bobée 《水文研究》2007,21(1):21-34
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
4.
Continuous temperature measurements at 11 stream sites in small lowland streams of North Zealand, Denmark over a year showed much higher summer temperatures and lower winter temperatures along the course of the stream with artificial lakes than in the stream without lakes. The influence of lakes was even more prominent in the comparisons of colder lake inlets and warmer outlets and led to the decline of cold‐water and oxygen‐demanding brown trout. Seasonal and daily temperature variations were, as anticipated, dampened by forest cover, groundwater input, input from sewage plants and high downstream discharges. Seasonal variations in daily water temperature could be predicted with high accuracy at all sites by a linear air‐water regression model (r2: 0·903–0·947). The predictions improved in all instances (r2: 0·927–0·964) by a non‐linear logistic regression according to which water temperatures do not fall below freezing and they increase less steeply than air temperatures at high temperatures because of enhanced heat loss from the stream by evaporation and back radiation. The predictions improved slightly (r2: 0·933–0·969) by a multiple regression model which, in addition to air temperature as the main predictor, included solar radiation at un‐shaded sites, relative humidity, precipitation and discharge. Application of the non‐linear logistic model for a warming scenario of 4–5 °C higher air temperatures in Denmark in 2070‐2100 yielded predictions of temperatures rising 1·6–3·0 °C during winter and summer and 4·4–6·0 °C during spring in un‐shaded streams with low groundwater input. Groundwater‐fed springs are expected to follow the increase of mean air temperatures for the region. Great caution should be exercised in these temperature projections because global and regional climate scenarios remain open to discussion. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
5.
Water temperature influences most of the physical, chemical and biological properties of rivers. It plays an important role in the distribution of fish and the growth rates of many aquatic organisms. Therefore, a better understanding of the thermal regime of rivers is essential for the management of important fisheries resources. This study deals with the modelling of river water temperature using a new and simplified model based on the equilibrium temperature concept. The equilibrium temperature concept is an approach where the net heat flux at the water surface can be expressed by a simple equation with fewer meteorological parameters than required with traditional models. This new water temperature model was applied on two watercourses of different size and thermal characteristics, but within a similar meteorological region, i.e., the Little Southwest Miramichi River and Catamaran Brook (New Brunswick, Canada). A study of the long‐term thermal characteristics of these two rivers revealed that the greatest differences in water temperatures occurred during mid‐summer peak temperatures. Data from 1992 to 1994 were used for the model calibration, while data from 1995 to 1999 were used for the model validation. Results showed a slightly better agreement between observed and predicted water temperatures for Catamaran Brook during the calibration period, with a root‐mean‐square error (RMSE) of 1·10 °C (Nash coefficient, NTD = 0·95) compared to 1·45 °C for the Little Southwest Miramichi River (NTD = 0·94). During the validation period, RMSEs were calculated at 1·31 °C for Catamaran Brook and 1·55 °C for the Little Southwest Miramichi River. Poorer model performances were generally observed early in the season (e.g., spring) for both rivers due to the influence of snowmelt conditions, while late summer to autumn modelling performances showed better results. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
6.
Historical records of monthly streamflow and precipitation coupled with mean, minimum, and maximum air temperatures for Washington State were used to study the variation and the trend characteristics that occurred over the last 50 years (1952–2002). Results indicate that the 1967 statewide water resource assessment needs to be updated because all of the stations used in that study exhibited a decreasing trend in annual streamflow ranging from ?0·9% to ?49·3%, with an arithmetic mean of ?11·7% and a median value of ?9·8%. Furthermore, a slightly decreasing trend in annual streamflow, although not statistically significant, was detected. The decreasing streamflow magnitude was about ?1·178 mm year?2, or 4·88 m3 s?1 year?1, which caused a decrease in annual streamflow in the state of about 58·9 mm, or 244 m3 s?1. This magnitude was about 9·6% of the average annual streamflow for the entire state from 1952 to 2002. Contrastingly, the overall annual precipitation in the entire state increased 1·375 mm year?2. Overall the annual means of daily mean, maximum, and minimum temperature increased by 0·122, 0·048, and 0·185 °C/10 years, respectively, during the study period. Thus the corresponding annual means of daily mean, maximum, and minimum temperatures increased by 0·61, 0·24, and 0·93 °C, respectively. All of these trends and magnitudes were found to vary considerably from station to station and month to month. The possible reasons resulting in these detected trends include, but are not limited to, human activities, climate variability and changes, and land use and land cover changes. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
7.
André St‐Hilaire Taha B. M. J. Ouarda Zoubeida Bargaoui Anik Daigle Laurent Bilodeau 《水文研究》2012,26(9):1302-1310
Water temperature is a key abiotic variable that modulates both water chemistry and aquatic life in rivers and streams. For this reason, numerous water temperature models have been developed in recent years. In this paper, a k‐nearest neighbour model (KNN) is proposed and validated to simulate and eventually produce a one‐day forecast of mean water temperature on the Moisie River, a watercourse with an important salmon population in eastern Canada. Numerous KNN model configurations were compared by selecting different attributes and testing different weight combinations for neighbours. It was found that the best model uses attributes that include water temperature from the two previous days and an indicator of seasonality (day of the year) to select nearest neighbours. Three neighbours were used to calculate the estimated temperature, and the weighting combination that yielded the best results was an equal weight on all three nearest neighbours. This nonparametric model provided lower Root Mean Square Errors (RMSE = 1·57 °C), Higher Nash coefficient (NTD = 0·93) and lower Relative Bias (RB = ? 1·5%) than a nonlinear regression model (RMSE = 2·45 °C, NTD = 0·83, RB = ? 3%). The k‐nearest neighbour model appears to be a promising tool to simulate of forecast water temperature where long time series are available. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
8.
Vassilis Z. Antonopoulos Soultana K. Gianniou Athanasios V. Antonopoulos 《水文科学杂志》2013,58(14):2590-2599
ABSTRACTEvaporation is one of the most important components in the energy and water budgets of lakes and is a primary process of water loss from their surfaces. An artificial neural network (ANN) technique is used in this study to estimate daily evaporation from Lake Vegoritis in northern Greece and is compared with the classical empirical methods of Penman, Priestley-Taylor and the mass transfer method. Estimation of the evaporation over the lake is based on the energy budget method in combination with a mathematical model of water temperature distribution in the lake. Daily datasets of air temperature, relative humidity, wind velocity, sunshine hours and evaporation are used for training and testing of ANN models. Several input combinations and different ANN architectures are tested to detect the most suitable model for predicting lake evaporation. The best structure obtained for the ANN evaporation model is 4-4-1, with root mean square error (RMSE) from 0.69 to 1.35 mm d?1 and correlation coefficient from 0.79 to 0.92.
EDITOR M.C. AcremanASSOCIATE EDITOR not assigned 相似文献
9.
K. P. Paaijmans A. F. G. Jacobs W. Takken B. G. Heusinkveld A. K. Githeko M. Dicke A. A. M. Holtslag 《水文研究》2008,22(24):4789-4801
Water temperature is an important determinant of the growth and development of malaria mosquito immatures. To gain a better understanding of the daily temperature dynamics of malaria mosquito breeding sites and of the relationships between meteorological variables and water temperature, three clear water pools (diameter × depth: 0·16 × 0·04, 0·32 × 0·16 and 0·96 × 0·32 m) were created in Kenya. Continuous water temperature measurements at various depths were combined with weather data collections from a meteorological station. The water pools were homothermic, but the top water layer differed by up to about 2 °C in temperature, depending on weather conditions. Although the daily mean temperature of all water pools was similar (27·4–28·1 °C), the average recorded difference between the daily minimum and maximum temperature was 14·4 °C in the smallest versus 7·1 °C in the largest water pool. Average water temperature corresponded well with various meteorological variables. The temperature of each water pool was continuously higher than the air temperature. A model was developed that predicts the diurnal water temperature dynamics accurately, based on the estimated energy budget components of these water pools. The air–water interface appeared the most important boundary for energy exchange processes and on average 82–89% of the total energy was gained and lost at this boundary. Besides energy loss to longwave radiation, loss due to evaporation was high; the average estimated daily evaporation ranged from 4·2 mm in the smallest to 3·7 mm in the largest water pool. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
10.
Evaluating performances of four commonly used evaporation estimate methods, namely; Bowen ratio energy balance (BREB), mass transfer (MT), Priestley–Taylor (PT) and pan evaporation (PE), based on 4 years experimental data, the most effective and the reliable evaporation estimates model for the semi‐arid region of India has been derived. The various goodness‐of‐fit measures, such as; coefficient of determination (R2), index of agreement (D), root mean square error (RMSE), and relative bias (RB) have been chosen for the performance evaluation. Of these models, the PT model has been found most promising when the Bowen ratio, β is known a priori, and based on its limited data requirement. The responses of the BREB, the PT, and the PE models were found comparable to each other, while the response of the MT model differed to match with the responses of the other three models. The coefficients, β of the BREB, µ of the MT, α of the PT and KP of the PE model were estimated as 0·07, 2·35, 1·31 and 0·65, respectively. The PT model can successfully be extended for free water surface evaporation estimates in semi‐arid India. A linear regression model depicting relationship between daily air and water temperature has been developed using the observed water temperatures and the corresponding air temperatures. The model helped to generate unrecorded water temperatures for the corresponding ambient air temperatures. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
11.
Stream temperature will be subject to changes because of atmospheric warming in the future. We investigated the effects of the diurnal timing of air temperature changes – daytime warming versus nighttime warming – on stream temperature. Using the physically based model, Heat Source, we performed a sensitivity analysis of summer stream temperatures to three diurnal air temperature distributions of +4 °C mean air temperature: i) uniform increase over the whole day, ii) warmer daytime and iii) warmer nighttime. The stream temperature model was applied to a 37‐km section of the Middle Fork John Day River in northeastern Oregon, USA. The three diurnal air temperature distributions generated 7‐day average daily maximum stream temperatures increases of approximately +1.8 °C ± 0.1 °C at the downstream end of the study section. The three air temperature distributions, with the same daily mean, generated different ranges of stream temperatures, different 7‐day average daily maximum temperatures, different durations of stream temperature changes and different average daily temperatures in most parts of the reach. The stream temperature changes were out of phase with air temperature changes, and therefore in many places, the greatest daytime increase in stream temperature was caused by nighttime warming of air temperatures. Stream temperature changes tended to be more extreme and of longer duration when driven by air temperatures concentrated in either daytime or nighttime instead of uniformly distributed across the diurnal cycle. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
12.
13.
Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed 总被引：1，自引：0，他引：1
This paper reports on an evaluation of the use of artificial neural network (ANN) models to forecast daily flows at multiple gauging stations in Eucha Watershed, an agricultural watershed located in north‐west Arkansas and north‐east Oklahoma. Two different neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBFNN), were developed and their abilities to predict stream flow at four gauging stations were compared. Different scenarios using various combinations of data sets such as rainfall and stream flow at various lags were developed and compared for their ability to make flow predictions at four gauging stations. The input vector selection for both models involved quantification of the statistical properties such as cross‐, auto‐ and partial autocorrelation of the data series that best represented the hydrologic response of the watershed. Measured data with 739 patterns of input–output vector were divided into two sets: 492 patterns for training, and the remaining 247 patterns for testing. The best performance based on the RMSE, R2 and CE was achieved by the MLP model with current and antecedent precipitation and antecedent flow as model inputs. The MLP model testing resulted in R2 values of 0·86, 0·86, 0·81, and 0·79 at the four gauging stations. Similarly, the testing R2 values for the RBFNN model were 0·60, 0·57, 0·58, and 0·56 for the four gauging stations. Both models performed satisfactorily for flow predictions at multiple gauging stations, however, the MLP model outperformed the RBFNN model. The training time was in the range 1–2 min for MLP, and 5–10 s for RBFNN on a Pentium IV processor running at 2·8 GHz with 1 MB of RAM. The difference in model training time occurred because of the clustering methods used in the RBFNN model. The RBFNN uses a fuzzy min‐max network to perform the clustering to construct the neural network which takes considerably less time than the MLP model. Results show that ANN models are useful tools for forecasting the hydrologic response at multiple points of interest in agricultural watersheds. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
14.
Forecasting river flow is important to water resources management and planning. In this study, an artificial neural network (ANN) model was successfully developed to forecast river flow in Apalachicola River. The model used a feed‐forward, back‐propagation network structure with an optimized conjugated training algorithm. Using long‐term observations of rainfall and river flow during 1939–2000, the ANN model was satisfactorily trained and verified. Model predictions of river flow match well with the observations. The correlation coefficients between forecasting and observation for daily, monthly, quarterly and yearly flow forecasting are 0·98, 0·95, 0·91 and 0·83, respectively. Results of the forecasted flow rates from the ANN model were compared with those from a traditional autoregressive integrated moving average (ARIMA) forecasting model. Results indicate that the ANN model provides better accuracy in forecasting river flow than does the ARIMA model. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
15.
Fikret Inal 《洁净——土壤、空气、水》2010,38(10):897-908
Tropospheric (ground‐level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1‐h average ozone concentrations in Istanbul were predicted using multi‐layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross‐validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d2) for the testing data were 0.90, 8.78 µg/m3, 11.15 µg/m3, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM10, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non‐linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul. 相似文献
16.
In conjunction with available climate data, surface runoff is investigated at 12 gauges in the Quesnel watershed of British Columbia to develop its long‐term (1926–2004) hydroclimatology. At Quesnel itself, annual mean values of air temperature, precipitation and runoff are 4·6 °C, 517 and 648 mm, respectively. Climate data reveal increases in precipitation, no significant trend in mean annual air temperature, but an increasing trend in mean minimum temperatures that is greatest in winter. There is some evidence of decreases in winter snow depth. On the water year scale (October–September), a strong positive correlation is found between discharge and precipitation (r = 0·70, p < 0·01) and a weak negative correlation is found between precipitation and temperature (r = ? 0·36, p < 0·01). Long‐term trends using the Mann‐Kendall test indicate increasing annual discharge amounts that vary from 8 to 14% (12% for the Quesnel River, p = 0·03), and also a tendency toward an earlier spring freshet. River runoff increases at a rate of 1·26 mm yr?1 m?1 of elevation from west to east along the strong elevation gradient in the basin. Discharge, temperature and precipitation are correlated with the large‐scale climate indices of the Pacific Decadal Oscillation (PDO) and El‐Niño Southern Oscillation (ENSO). Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
17.
The active layer of frozen ground data assimilation system adopts the SHAW (Simulteneous Heat and Water) model as the model
operator. It employs an ensemble kalman filter to fuse state variables predicted by the SHAW model with in situ observation and the SSM/I 19 GHz brightness temperature for the purpose of optimizing model hydrothermal state variables.
When there is little water movement in the frozen soil during the winter season, the unfrozen water content depends primarily
on soil temperature. Thus, soil temperature is the crucial state variable to be improved. In contrast, soil moisture is heavily
influenced by precipitation during the summer season. The simulation accuracy of soil moisture has a strong and direct impact
on the soil temperature. In this case, the crucial state variable to be improved is soil moisture. One-dimensional assimilation
experiments that have been carried out at AMDO station show that land data assimilation method can improve the estimation
of hydrothermal state variables in the soil by fusing model information and observation information. The reasonable model
error covariance matrix plays a key role in transferring the optimized surface state information to the deep soil, and it
provides improved estimations of whole soil state profiles. After assimilating the 4-cm soil temperature by in situ observation, the soil temperature RMSE (Root Mean Square Error) of each soil layer decreased by 0.96°C on average relative
to the SHAW simulation. After assimilating the 4-cm soil moisture in situ observation, the soil moisture RMSE of each soil layer decreased by 0.020 m3·m−3. When assimilating the SSM/I 19 GHz brightness temperature, the soil temperature RMSE of each soil layer during the winter
decreased by 0.76°C, while the soil moisture RMSE of each soil layer during the summer decreased by 0.018 m3·m−3. 相似文献
18.
Groundwater is sensitive to the climate change and agricultural activities in arid and semi‐arid areas. Over the past several decades, human activities, such as groundwater extraction for irrigation, have resulted in aquifer overdraft and disrupted the natural equilibrium in these areas. Regional groundwater simulation is important to determine appropriate groundwater management policies, and numerical simulation has become the most popular method. However, most groundwater models were developed with static boundary conditions. In this research, the Minqin oasis, an arid region located in northwest China, was selected as the study area. An artificial neural network (ANN) was developed to simulate effects of weather conditions, agricultural activities and surface water on groundwater level in a dynamic boundary of the domain. Subsequently, a groundwater numerical model, named ANN‐FEFLOW model, was developed, with a dynamic boundary condition defined by the ANN model. The verifying results showed that the model has higher precision, with a root mean square error (RMSE) of 0·71 m, relative error (RE) of 17·96% and R2 of 0·84 relative to the great groundwater change. Furthermore, the groundwater model has higher precision than the conventional groundwater model with static boundary condition, particularly in the area near the dynamic boundary. This study demonstrated that dynamic boundaries can improve the precision of the regional groundwater model in an arid area and that ANN can provide higher accuracy prediction capability for groundwater levels with dynamic boundary. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
19.
Suitability of North American Regional Reanalysis (NARR) output for hydrologic modelling and analysis in mountainous terrain 
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下载免费PDF全文 Meteorological observations at high elevations in mountainous regions are often lacking. One opportunity to fill this data gap is through the use of downscaled output from weather reanalysis models. In this study, we tested the accuracy of downscaled output from the North American Regional Reanalysis (NARR) against high‐elevation surface observations at four ridgetop locations in the southern Coast Mountains of British Columbia, Canada. NARR model output was downscaled to the surface observation locations through three‐dimensional interpolation for air temperature, vapour pressure and wind speed and two‐dimensional interpolation for radiation variables. Accuracy was tested at both the 3‐hourly and daily time scales. Air temperature displayed a high level of agreement, especially at the daily scale, with root mean square error (RMSE) values ranging from 0.98 to 1.21 °C across all sites. Vapour pressure downscaling accuracy was also quite high (RMSE of 0.06 to 0.11 hPa) but displayed some site specific bias. Although NARR overestimated wind speed, there were moderate to strong linear relations (r2 from 0.38 to 0.84 for daily means), suggesting that the NARR output could be used as an index and bias‐corrected. NARR output reproduced the seasonal cycle for incoming short‐wave radiation, with Nash–Sutcliffe model efficiencies ranging from 0.78 to 0.87, but accuracy suffered on days with cloud cover, resulting in a positive bias and RMSE ranged from 42 to 46 Wm? 2. Although fewer data were available, incoming long‐wave radiation from NARR had an RMSE of 19 Wm? 2 and outperformed common methods for estimating incoming long‐wave radiation. NARR air temperature showed potential to assist in hydrologic analysis and modelling during an atmospheric river storm event, which are characterized by warm and wet air masses with atypical vertical temperature gradients. The incorporation of a synthetic NARR air temperature station to better represent the higher freezing levels resulted in increased predicted peak flows, which better match the observed run‐off during the event. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
20.
Examining the effect of geomorphic characteristics on pool temperatures for native fish habitat management in mountainous stream networks 下载免费PDF全文
下载免费PDF全文 Stream temperature is a critical habitat parameter for cold‐water fish, many species of which now exist in geographically fragmented populations within the western United States. To assist managers in identifying thermally suitable fish habitat, we used data from 31 pools on streams of the White River National Forest in Colorado, USA to create multiple regression models to predict summer pool temperature metrics related to lethal and sublethal thermal tolerances of fish. We modeled the 7‐day mean of daily maximum pool temperature for the warmest 7 days and the mean temperature of the warmest month, using air temperature and several geomorphic parameters. The strongest predictor variables of these temperature metrics were drainage area, discharge, and residual pool volume. Most previous studies found air temperature to be the strongest predictor variable for pool temperature, but for the mountain streams in this study, variables related to stream flow volume and stream morphology had better predictive power. The models, created from and tested against field data, were able to explain 66% and 51% of the variability in monthly mean and 7‐day mean pool temperatures, respectively, and had prediction errors of less than 2°C. The reach‐scale approach developed here, which includes geomorphically relevant predictors of pool temperature, should be applicable to other mountainous river networks. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献