 |
| Titel |
Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model |
| VerfasserIn |
A. Gallice, B. Schaefli, M. Lehning, M. B. Parlange, H. Huwald |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 9 ; Nr. 19, no. 9 (2015-09-01), S.3727-3753 |
| Datensatznummer |
250120800
|
| Publikation (Nr.) |
 copernicus.org/hess-19-3727-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
| 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|