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1.
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
2.
Stream temperature modelling using artificial neural networks: application on Catamaran Brook,New Brunswick,Canada 总被引：2，自引：0，他引：2
Fish habitat and aquatic life in rivers are highly dependent on water temperature. Therefore, it is important to understand andto be able to predict river water temperatures using models. Such models can increase our knowledge of river thermal regimes as well as provide tools for environmental impact assessments. In this study, artificial neural networks (ANNs) will be used to develop models for predicting both the mean and maximum daily water temperature. The study was conducted within Catamaran Brook, a small drainage basin tributary to the Miramichi River (New Brunswick, Canada). In total, eight ANN models were investigated using a variety of input parameters. Of these models, four predicted mean daily water temperature and four predicted maximum daily water temperature. The best model for mean daily temperature had eight input parameters: minimum, maximum and mean air temperatures of the current day and those of the preceding day, the day of year and the water level. This model had an overall root‐mean‐square error (RMSE) of 0·96 °C, a bias of 0·26 °C and a coefficient of determination R2 = 0·971. The model that best predicted maximum daily water temperature was similar to the first model but excluded mean daily air temperature. Good results were obtained for maximum water temperatures with an overall RMSE of 1·18 °C, a bias of 0·15 °C and R2 = 0·961. The results of ANN models were similar to and/or better than those observed from the literature. The advantages of artificial neural networks models in modelling river water temperature lie in their simplicity of use, their low data requirement and their good performance, as well as their flexibility in allowing many input and output parameters. Copyright © 2008 Crown in the right of Canada and John Wiley & Sons, Ltd. 相似文献
3.
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
4.
Insights on stream temperature processes through development of a coupled hydrologic and stream temperature model for forested coastal headwater catchments 
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下载免费PDF全文 Stream temperature controls a number of biological, chemical, and physical processes occurring in aquatic environments. Transient snow cover and advection associated with lateral throughflow inputs can have a dominant influence on stream thermal regimes for headwater catchments in the rain‐on‐snow zone. Most existing stream temperature models lack the ability to properly simulate these processes. We developed and evaluated a conceptual‐parametric catchment‐scale stream temperature model that includes the role of transient snow cover and lateral advection associated with throughflow. The model consists of routines for simulating canopy interception, snow accumulation and melt, hillslope throughflow runoff and temperature, and stream channel energy exchange processes. The model was used to predict discharge and stream temperature for a small forested headwater catchment near Vancouver, Canada, using long‐term (1963–2013) weather data to compute model forcing variables. The model was evaluated against 4 years of observed stream temperature. The model generally predicted daily mean stream temperature accurately (annual RMSE between 0.57 and 1.24 °C) although it overpredicted daily summer stream temperatures by up to 3 °C during extended low streamflow conditions. Model development and testing provided insights on the roles of advection associated with lateral throughflow, channel interception of snow, and surface–subsurface water interactions on stream thermal regimes. This study shows that a relatively simple but process‐based model can provide reasonable stream temperature predictions for forested headwater catchments located in the rain‐on‐snow zone. 相似文献
5.
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
6.
《水文科学杂志》2013,58(3):640-655
Abstract Water temperature is an important abiotic variable in aquatic habitat studies and may be one of the factors limiting the potential fish habitat (e.g. salmonids) in a stream. Stream water temperatures are modelled using statistical approaches with air temperature and streamflow as exogenous variables in the Nivelle River, southern France. Two different models are used to model mean weekly maximum temperature data: a non-parametric approach, the k-nearest neighbours method (k-NN) and a parametric approach, the periodic autoregressive model with exogenous variables (PARX). The k-NN is a data-driven method, which consists of finding, at each point of interest, a small number of neighbours nearest to this value, and the prediction is estimated based on these neighbours. The PARX model is an extension of commonly-used autoregressive models in which parameters are estimated for each period within the years. Different variants of air temperature and flow are used in the model development. In order to test the performance of these models, a jack-knife technique is used, whereby model goodness of fit is assessed separately for each year. The results indicate that both models give good performances, but the PARX model should be preferred, because of its good estimation of the individual weekly temperatures and its ability to explicitly predict water temperature using exogenous variables. 相似文献
7.
Water temperature has a significant influence on aquatic organisms, including stenotherm fish such as salmonids. It is thus of prime importance to build reliable tools to forecast water temperature. This study evaluated a statistical scheme to model average water temperature based on daily average air temperature and average discharge at the Sainte-Marguerite River, Northern Canada. The aim was to test a non-parametric water temperature generalized additive model (GAM) and to compare its performance to three previously developed approaches: the logistic, residuals regression and linear regression models. Due to its flexibility, the GAM was able to capture some of the nonlinear response between water temperature and the two explanatory variables (air temperature and flow). The shape of these effects was determined by the trends shown in the collected data. The four models were evaluated annually using a cross-validation technique. Three comparison criteria were calculated: the root mean square error (RMSE), the bias error and the Nash-Sutcliffe coefficient of efficiency (NSC). The goodness of fit of the four models was also compared graphically. The GAM was the best among the four models (RMSE = 1.44°C, bias = ?0.04 and NSC = 0.94). 相似文献
8.
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
9.
The importance of incorporating diurnally fluctuating stream discharge in stream temperature energy balance models 下载免费PDF全文
下载免费PDF全文 Although stream temperature energy balance models are useful to predict temperature through time and space, a major unresolved question is whether fluctuations in stream discharge reduce model accuracy when not exactly represented. However, high‐frequency (e.g., subdaily) discharge observations are often unavailable for such simulations, and therefore, diurnal streamflow fluctuations are not typically represented in energy balance models. These fluctuations are common due to evapotranspiration, snow pack or glacial melt, tidal influences within estuaries, and regulated river flows. In this work, we show when to account for diurnally fluctuating streamflow. To investigate how diurnal streamflow fluctuations affect predicted stream temperatures, we used a deterministic stream temperature model to simulate stream temperature along a reach in the Quilcayhuanca Valley, Peru, where discharge varies diurnally due to glacial melt. Diurnally fluctuating streamflow was varied alongside groundwater contributions via a series of computational experiments to assess how uncertainty in reach hydrology may impact simulated stream temperature. Results indicated that stream temperatures were more sensitive to the rate of groundwater inflow to the reach compared with the timing and amplitude of diurnal fluctuations in streamflow. Although incorporating observed diurnal fluctuations in discharge resulted in a small improvement in model RMSE, we also assessed other diurnal discharge signals and found that high amplitude signals were more influential on modelled stream temperatures when the discharge peaked at specific times. Results also showed that regardless of the diurnal discharge signal, the estimated groundwater flux to the reach only varied from 1.7% to 11.7% of the upstream discharge. However, diurnal discharge fluctuations likely have a stronger influence over longer reaches and in streams where the daily range in discharge is larger, indicating that diurnal fluctuations in stream discharge should be considered in certain settings. 相似文献
10.
Air/water/sediment temperature contrasts in small streams to identify groundwater seepage locations 下载免费PDF全文
下载免费PDF全文 The need to identify groundwater seepage locations is of great importance for managing both stream water quality and groundwater sourced ecosystems due to their dependency on groundwater‐borne nutrients and temperatures. Although several reconnaissance methods using temperature as tracer exist, these are subjected to limitations related to mainly the spatial and temporal resolution and/or mixing of groundwater and surface water leading to dilution of the temperature differences. Further, some methods, for example, thermal imagery and fiber optic distributed temperature sensing, although relative efficient in detecting temperature differences over larger distances, these are labor‐intensive and costly. Therefore, there is a need for additional cost‐effective methods identifying substantial groundwater seepage locations. We present a method expanding the linear regression of air and stream temperatures by measuring the temperatures in dual‐depth; in the stream column and at the streambed‐water interface (SWI). By doing so, we apply metrics from linear regression analysis of temperatures between air/stream and air/SWI (linear regression slope, intercept, and coefficient of determination), and the daily water temperature cycle (daily mean temperatures, temperature variance, and the mean diel temperature fluctuation). We show that using metrics from only single‐depth stream temperature measurements are insufficient to identify substantial groundwater seepage locations in a head‐water stream. Conversely, comparing the metrics from dual‐depth temperatures show significant differences; at groundwater seepage locations, temperatures at the SWI merely explain 43–75% of the variation opposed to ? 91% at the corresponding stream column temperatures. In general, at these locations at the SWI, the slopes ( < 0.25) and intercepts ( > 6.5 °C) are substantially lower and higher, respectively, while the mean diel temperature fluctuations ( < 0.98 °C) are decreased compared to remaining locations. The dual‐depth approach was applied in a post‐glacial fluvial setting, where metrics analyses overall corroborated with field measurements of groundwater fluxes and stream flow accretions. Thus, we propose a method reliably identifying groundwater seepage locations along streambeds in such settings. 相似文献
11.
Many methods developed for calibration and validation of physically based distributed hydrological models are time consuming and computationally intensive. Only a small set of input parameters can be optimized, and the optimization often results in unrealistic values. In this study we adopted a multi‐variable and multi‐site approach to calibration and validation of the Soil Water Assessment Tool (SWAT) model for the Motueka catchment, making use of extensive field measurements. Not only were a number of hydrological processes (model components) in a catchment evaluated, but also a number of subcatchments were used in the calibration. The internal variables used were PET, annual water yield, daily streamflow, baseflow, and soil moisture. The study was conducted using an 11‐year historical flow record (1990–2000); 1990–94 was used for calibration and 1995–2000 for validation. SWAT generally predicted well the PET, water yield and daily streamflow. The predicted daily streamflow matched the observed values, with a Nash–Sutcliffe coefficient of 0·78 during calibration and 0·72 during validation. However, values for subcatchments ranged from 0·31 to 0·67 during calibration, and 0·36 to 0·52 during validation. The predicted soil moisture remained wet compared with the measurement. About 50% of the extra soil water storage predicted by the model can be ascribed to overprediction of precipitation; the remaining 50% discrepancy was likely to be a result of poor representation of soil properties. Hydrological compensations in the modelling results are derived from water balances in the various pathways and storage (evaporation, streamflow, surface runoff, soil moisture and groundwater) and the contributions to streamflow from different geographic areas (hill slopes, variable source areas, sub‐basins, and subcatchments). The use of an integrated multi‐variable and multi‐site method improved the model calibration and validation and highlighted the areas and hydrological processes requiring greater calibration effort. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
12.
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
13.
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
14.
Mohammad M. Sohrabi Rohan Benjankar Daniele Tonina Seth J. Wenger Daniel J. Isaak 《水文研究》2017,31(9):1719-1733
Stream water temperature plays a significant role in aquatic ecosystems where it controls many important biological and physical processes. Reliable estimates of water temperature at the daily time step are critical in managing water resources. We developed a parsimonious piecewise Bayesian model for estimating daily stream water temperatures that account for temporal autocorrelation and both linear and nonlinear relationships with air temperature and discharge. The model was tested at 8 climatically different basins of the USA and at 34 sites within the mountainous Boise River Basin (Idaho, USA). The results show that the proposed model is robust with an average root mean square error of 1.25 °C and Nash–Sutcliffe coefficient of 0.92 over a 2‐year period. Our approach can be used to predict historic daily stream water temperatures in any location using observed daily stream temperature and regional air temperature data. 相似文献
15.
River water quality response under hypothetical climate change scenarios in Tunga‐Bhadra river,India
Analysis of climate change impacts on streamflow by perturbing the climate inputs has been a concern for many authors in the past few years, but there are few analyses for the impacts on water quality. To examine the impact of change in climate variables on the water quality parameters, the water quality input variables have to be perturbed. The primary input variables that can be considered for such an analysis are streamflow and water temperature, which are affected by changes in precipitation and air temperature, respectively. Using hypothetical scenarios to represent both greenhouse warming and streamflow changes, the sensitivity of the water quality parameters has been evaluated under conditions of altered river flow and river temperature in this article. Historical data analysis of hydroclimatic variables is carried out, which includes flow duration exceedance percentage (e.g. Q90), single low‐flow indices (e.g. 7Q10, 30Q10) and relationships between climatic variables and surface variables. For the study region of Tunga‐Bhadra river in India, low flows are found to be decreasing and water temperatures are found to be increasing. As a result, there is a reduction in dissolved oxygen (DO) levels found in recent years. Water quality responses of six hypothetical climate change scenarios were simulated by the water quality model, QUAL2K. A simple linear regression relation between air and water temperature is used to generate the scenarios for river water temperature. The results suggest that all the hypothetical climate change scenarios would cause impairment in water quality. It was found that there is a significant decrease in DO levels due to the impact of climate change on temperature and flows, even when the discharges were at safe permissible levels set by pollution control agencies (PCAs). The necessity to improve the standards of PCA and develop adaptation policies for the dischargers to account for climate change is examined through a fuzzy waste load allocation model developed earlier. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
16.
Stream temperature, an important measure of ecosystem health, is expected to be altered by future changes in climate and land use, potentially leading to shifts in habitat distribution for aquatic organisms dependent on particular temperature regimes. To assess the sensitivity of stream temperature to change in a region where such a shift has the potential to occur, we examine the variability of and controls on the direct relationship between air and water temperature across the state of Pennsylvania. We characterized the relationship between air and stream temperature via linear and nonlinear regression for 57 sites across Pennsylvania at daily and weekly timescales. Model fit (r2) improved for 92% (daily) and 65% (weekly) of sites for nonlinear versus linear relationships. Fit for weekly versus daily regression analysis improved by 0·08 for linear and 0·06 for nonlinear regression relationships. To investigate the mechanisms controlling stream temperature sensitivity to environmental change, we define ‘thermal sensitivity’ as the sensitivity of stream temperature of a given site to change in air temperature, quantified as the slope of the regression line between air and stream temperature. Air temperature accounted for 60–95% of the daily variation in stream temperature for sites at or above a Strahler stream order (SO) of 3, with thermal sensitivities ranging from low (0·02) to high (0·93). The sensitivity of stream temperature to air temperature is primarily controlled by stream size (SO) and baseflow contribution. Together, SO and baseflow index explained 43% of the variance in thermal sensitivity across the state, and 59% within the Susquehanna River Basin. In small streams, baseflow contribution was the major determinant of thermal sensitivity, with increasing baseflow contributions resulting in decreasing sensitivity values. In large streams, thermal sensitivity increased with stream size, as a function of accumulated heat throughout the stream network. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
17.
The nature of the water–air temperature relationship, and its moderation by discharge, were investigated for catchments ranging in size from 2·1 to 601 km2 in the Exe basin, Devon, UK and for data relating to hourly, daily and weekly time bases. The sensitivity and explanatory power of simple water–air temperature regression models based on hourly data were improved by incorporation of a lag, which increased with catchment size, although relationships became more sensitive and less scattered as the time base of data increased from hourly to weekly mean values. Significant departures from linearity in water–air temperature relationships were evident for hourly, but not for daily mean or weekly mean, data. A clear tendency for relationships between water and air temperatures to be stronger and more sensitive for flows below median levels was apparent, and multiple regression analysis also revealed water temperature to be inversely related to discharge for all catchments and time‐scales. However, discharge had a greater impact in accounting for water temperature variation at shorter time‐scales and in larger catchments. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
18.
Water temperatures in mountain streams are likely to rise under future climate change, with negative impacts on ecosystems and water quality. However, it is difficult to predict which streams are most vulnerable due to sparse historical records of mountain stream temperatures as well as complex interactions between snowpack, groundwater, streamflow and water temperature. Minimum flow volumes are a potentially useful proxy for stream temperature, since daily streamflow records are much more common. We confirmed that there is a strong inverse relationship between annual low flows and peak water temperature using observed data from unimpaired streams throughout the montane regions of the United States' west coast. We then used linear models to explore the relationships between snowpack, potential evapotranspiration and other climate-related variables with annual low flow volumes and peak water temperatures. We also incorporated previous years' flow volumes into these models to account for groundwater carryover from year to year. We found that annual peak snowpack water storage is a strong predictor of summer low flows in the more arid watersheds studied. This relationship is mediated by atmospheric water demand and carryover subsurface water storage from previous years, such that multi-year droughts with high evapotranspiration lead to especially low flow volumes. We conclude that watershed management to help retain snow and increase baseflows may help counteract some of the streamflow temperature rises expected from a warming climate, especially in arid watersheds. 相似文献
19.
Measurements of sap flow, meteorological parameters, soil water content and tension were made for 4 months in a young cashew (Anacardium occidentale L.) plantation during the 2002 rainy season in Ejura, Ghana. This experiment was part of a sustainable water management project in West Africa. The Granier system was used to measure half‐hourly whole‐tree sap flow. Weather variables were observed with an automatic weather station, whereas soil moisture and tension were measured with a Delta‐T profile probe and tensiometers respectively. Clearness index (CI), a measure of the sky condition, was significantly correlated with tree transpiration (r2 = 0·73) and potential evaporation (r2 = 0·86). Both diurnal and daily stomata conductance were poorly correlated with the climatic variables. Estimated daily canopy conductance gc ranged from 4·0 to 21·2 mm s−1, with a mean value of 8·0 ± 3·3 mm s−1. Water flux variation was related to a range of environmental variables: soil water content, air temperature, solar radiation, relative humidity and vapour pressure deficit. Linear and non‐linear regression models, as well as a modified Priestley–Taylor formula, were fitted with transpiration, and the well‐correlated variables, using half‐hourly measurements. Measured and predicted transpiration using these regression models were in good agreement, with r2 ranging from 0·71 to 0·84. The computed measure of accuracy δ indicated that a non‐linear model is better than its corresponding linear one. Furthermore, solar radiation, CI, clouds and rain were found to influence tree water flux. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
20.
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