%0 Journal Article %1 wanders2019highresolution %A Wanders, Niko %A van Vliet, Michelle T. H. %A Wada, Yoshihide %A Bierkens, Marc F. P. %A van Beek, Ludovicus P. H. (Rens) %D 2019 %J Water Resources Research %K data, hydrological\_modelling, rivers, %R 10.1029/2018WR023250 %T High-Resolution Global Water Temperature Modeling %U https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR023250 %V 55 %X Abstract The temperature of river water plays a crucial role in many physical, chemical, and aquatic ecological processes. Despite the importance of having detailed information on this environmental variable at locally relevant scales (≤50 km), high-resolution simulations of water temperature on a large scale are currently lacking. We have developed the dynamical 1-D water energy routing model (DynWat), that solves both the energy and water balance, to simulate river temperatures for the period 1960–2014 at a nominal 10-km and 50-km resolution. The DynWat model accounts for surface water abstraction, reservoirs, riverine flooding, and formation of ice, enabling a realistic representation of the water temperature. We present a novel 10-km water temperature data set at the global scale for all major rivers, lakes, and reservoirs. Validated results against 358 stations worldwide indicate a decrease in the simulated root-mean-square error (0.2 °C) and bias (0.7 °C), going from 50- to 10-km simulations. We find an average global increase in water temperature of 0.16 °C per decade between 1960 and 2014, with more rapid warming toward 2014. Results show increasing trends for the annual daily maxima in the Northern Hemisphere (0.62 °C per decade) and the annual daily minima in the Southern Hemisphere (0.45 °C per decade) for 1960–2014. The high-resolution modeling framework not only improves the model performance, it also positively impacts the relevance of the simulations for regional-scale studies and impact assessments in a region without observations. The resulting global water temperature data set could help to improve the accuracy of decision-support systems that depend on water temperature estimates.

@article{wanders2019highresolution, abstract = {Abstract The temperature of river water plays a crucial role in many physical, chemical, and aquatic ecological processes. Despite the importance of having detailed information on this environmental variable at locally relevant scales (≤50 km), high-resolution simulations of water temperature on a large scale are currently lacking. We have developed the dynamical 1-D water energy routing model (DynWat), that solves both the energy and water balance, to simulate river temperatures for the period 1960–2014 at a nominal 10-km and 50-km resolution. The DynWat model accounts for surface water abstraction, reservoirs, riverine flooding, and formation of ice, enabling a realistic representation of the water temperature. We present a novel 10-km water temperature data set at the global scale for all major rivers, lakes, and reservoirs. Validated results against 358 stations worldwide indicate a decrease in the simulated root-mean-square error (0.2 °C) and bias (0.7 °C), going from 50- to 10-km simulations. We find an average global increase in water temperature of 0.16 °C per decade between 1960 and 2014, with more rapid warming toward 2014. Results show increasing trends for the annual daily maxima in the Northern Hemisphere (0.62 °C per decade) and the annual daily minima in the Southern Hemisphere (0.45 °C per decade) for 1960–2014. The high-resolution modeling framework not only improves the model performance, it also positively impacts the relevance of the simulations for regional-scale studies and impact assessments in a region without observations. The resulting global water temperature data set could help to improve the accuracy of decision-support systems that depend on water temperature estimates.}, added-at = {2019-04-30T16:57:43.000+0200}, author = {Wanders, Niko and van Vliet, Michelle T. H. and Wada, Yoshihide and Bierkens, Marc F. P. and van Beek, Ludovicus P. H. (Rens)}, biburl = {https://www.bibsonomy.org/bibtex/2a073f3aaab9be47235bc3e113b16e509/rutgerdankers}, doi = {10.1029/2018WR023250}, eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR023250}, interhash = {7ae0c3095d5f2b1c096c000717b238de}, intrahash = {a073f3aaab9be47235bc3e113b16e509}, journal = {Water Resources Research}, keywords = {data, hydrological\_modelling, rivers,}, timestamp = {2019-04-30T16:57:43.000+0200}, title = {High-Resolution Global Water Temperature Modeling}, url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR023250}, volume = 55, year = 2019 }