Exbrayat, J.F.; Buytaert, W.; Timbe, E.; Windhorst, D. & Breuer, L. (2014): <b>Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes</b>. <i>Climatic Change</i> <b>--</b>(--), 1-15.
Resource Description
Title:
Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes
FOR816dw ID:
1311
Publication Date:
2014-06-07
License and Usage Rights:
PAK 823-825 data user agreement. (www.tropicalmountainforest.org/dataagreementp3.do)
Resource Owner(s):
Individual:
Jean Francois Exbrayat
Contact:
email:
webmaster <at> tropicalmountainforest.org
Individual:
Wouter Buytaert
Contact:
email:
webmaster <at> tropicalmountainforest.org
Individual:
Edison Timbe
Contact:
email:
edison_timbe <at> yahoo.com
Victor Manuel Albornoz - Quinta Balzay
Universidad de Cuenca Cuenca
Ecuador
email:
lutz.breuer <at> umwelt.uni-giessen.de
Heinrich-Buff-Ring 26
Institute for Landscape Ecology and Resources Management
Justus-Liebig-University Gießen
35392 Gießen
Germany
Abstract:
Future climate projections from general circulation models (GCMs) predict an<br/>
acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data.<br/>
Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological
