High‐Resolution Global Water Temperature Modeling

Wanders, N., Vliet, M., Wada, Y. ORCID: https://orcid.org/0000-0003-4770-2539, Bierkens, ., & Beek, L. (2019). High‐Resolution Global Water Temperature Modeling. Water Resources Research 55 (4) 2760-2778. 10.1029/2018WR023250.

Preview

Text 2018WR023250.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (4MB) | Preview

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.

Actions (login required)

View Item