DOI: 10.5194/hess-19-3727-2015
Scopus记录号: 2-s2.0-84940844610
论文题名: Stream temperature prediction in ungauged basins: Review of recent approaches and description of a new physics-derived statistical model
作者: Gallice A ; , Schaefli B ; , Lehning M ; , Parlange M ; B ; , Huwald H
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2015
卷: 19, 期: 9 起始页码: 3727
结束页码: 3753
语种: 英语
Scopus关键词: Catchments
; Climate change
; Computer hardware description languages
; Data flow analysis
; Forecasting
; Linear regression
; Mean square error
; Regression analysis
; Energy balance equations
; Interannual variation
; Multi-linear regression
; Predictor variables
; Regression coefficient
; Root mean square errors
; Statistical modeling
; Statistical techniques
; Climate models
; alpine environment
; annual cycle
; anthropogenic effect
; catchment
; climate change
; energy balance
; prediction
; regression analysis
; statistical analysis
; streamwater
; water temperature
; Switzerland
英文摘要: The development of stream temperature regression models at regional scales has regained some popularity over the past years. These models are used to predict stream temperature in ungauged catchments to assess the impact of human activities or climate change on riverine fauna over large spatial areas. A comprehensive literature review presented in this study shows that the temperature metrics predicted by the majority of models correspond to yearly aggregates, such as the popular annual maximum weekly mean temperature (MWMT). As a consequence, current models are often unable to predict the annual cycle of stream temperature, nor can the majority of them forecast the inter-annual variation of stream temperature. This study presents a new statistical model to estimate the monthly mean stream temperature of ungauged rivers over multiple years in an Alpine country (Switzerland). Contrary to similar models developed to date, which are mostly based on standard regression approaches, this one attempts to incorporate physical aspects into its structure. It is based on the analytical solution to a simplified version of the energy-balance equation over an entire stream network. Some terms of this solution cannot be readily evaluated at the regional scale due to the lack of appropriate data, and are therefore approximated using classical statistical techniques. This physics-inspired approach presents some advantages: (1) the main model structure is directly obtained from first principles, (2) the spatial extent over which the predictor variables are averaged naturally arises during model development, and (3) most of the regression coefficients can be interpreted from a physical point of view - their values can therefore be constrained to remain within plausible bounds. The evaluation of the model over a new freely available data set shows that the monthly mean stream temperature curve can be reproduced with a root-mean-square error (RMSE) of ±1.3 °C, which is similar in precision to the predictions obtained with a multi-linear regression model. We illustrate through a simple example how the physical aspects contained in the model structure can be used to gain more insight into the stream temperature dynamics at regional scales. © Author(s) 2015. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78433
Appears in Collections: 气候变化事实与影响
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作者单位: School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; SLF, WSL Institute for Snow and Avalanche Research, Davos, Switzerland; Faculty of Applied Sciences, University of British Columbia, Vancouver, BC, Canada
Recommended Citation:
Gallice A,, Schaefli B,, Lehning M,et al. Stream temperature prediction in ungauged basins: Review of recent approaches and description of a new physics-derived statistical model[J]. Hydrology and Earth System Sciences,2015-01-01,19(9)