Open AccessStatistical framework for assessing uncertainty in hydrological models
Author(s) -
Schultz Colin
Publication year - 2013
Publication title -
eos, transactions american geophysical union
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.316
H-Index - 86
eISSN - 2324-9250
pISSN - 0096-3941
DOI - 10.1002/2013eo050016
Subject(s) - climate change , general circulation model , climate model , projection (relational algebra) , watershed , hydrological modelling , set (abstract data type) , environmental science , climatology , computer science , downscaling , econometrics , mathematics , geology , oceanography , algorithm , machine learning , programming language
For regional managers trying to make long‐term investments in hydrological infrastructure, having a reliable forecast of how their watershed may evolve in a changing climate is a significant boon. To make a projection of the regional effects of climate change, researchers often use the calculations of a global general circulation model to determine a set of initial conditions—for either historical or future climes—which can then be used to run a regional hydrological model. Using this approach, uncertainty can arise from a number of sources, including from the difficulties of projecting climate change, from errors within either the general circulation model or the hydrological model, from uncertainty surrounding modeled parameterizations, or from data sampling errors.