ALBERT

Description: Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physically-based analytical model Hydrology and Earth System Sciences Discussions, 12, 4081-4155, 2015 Author(s): A. Gallice, B. Schaefli, M. Lehning, M. P. Parlange, and H. Huwald 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 interannual variation of stream temperature. This study presents a new model to estimate the monthly mean stream temperature of ungauged rivers over multiple years in an Alpine country (Switzerland). Contrary to the models developed to date, which mostly rely upon statistical regression to express stream temperature as a function of physiographic and climatic variables, this one rests upon the analytical solution to a simplified version of the energy-balance equation over an entire stream network. This physically-based approach presents some advantages: (1) the functional form linking stream temperature to the predictor variables is directly obtained from first principles, (2) the spatial extent over which the predictor variables are averaged naturally arises during model development, and (3) 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 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 basis of the model can be used to gain more insight into the stream temperature dynamics at regional scales.