TY - JOUR
T1 - The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments
AU - Eghdamirad, Sajjad
AU - Johnson, Fiona
AU - Sharma, Ashish
AU - Kim, Joong Hoon
N1 - Funding Information:
This work was supported by a grant from National Research Foundation (NRF) of Korea, funded by the Korean government (MSIP) (No. 2016R1A2A1A05005306).
PY - 2019/4/26
Y1 - 2019/4/26
N2 - Few approaches exist that explicitly use the uncertainty associated with the spread of climate model simulations in assessing climate change impacts. An approach that does so is second-order approximation (SOA). This incorporates quantification of uncertainty to ascertain its impact on the derived response using a Taylor series expansion of the model. This study uses SOA in a statistical downscaling model of monthly streamflow, with a focus on the influence of dependence in the uncertainty of multiple atmospheric variables. Uncertainty is quantified using the square root error variance concept with a new extension that allows the inter-dependence terms among the atmospheric variable uncertainty to be specified. Applying the model to selected point locations in Australia, it is noted that the downscaling results differ considerably from downscaling that ignores uncertainty. However, when the effects of dependence in uncertainty are incorporated, the results differ according to the regional variations in dependence structure.
AB - Few approaches exist that explicitly use the uncertainty associated with the spread of climate model simulations in assessing climate change impacts. An approach that does so is second-order approximation (SOA). This incorporates quantification of uncertainty to ascertain its impact on the derived response using a Taylor series expansion of the model. This study uses SOA in a statistical downscaling model of monthly streamflow, with a focus on the influence of dependence in the uncertainty of multiple atmospheric variables. Uncertainty is quantified using the square root error variance concept with a new extension that allows the inter-dependence terms among the atmospheric variable uncertainty to be specified. Applying the model to selected point locations in Australia, it is noted that the downscaling results differ considerably from downscaling that ignores uncertainty. However, when the effects of dependence in uncertainty are incorporated, the results differ according to the regional variations in dependence structure.
KW - Taylor series
KW - climate variable uncertainty dependence
KW - statistical downscaling
KW - uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85065156594&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065156594&partnerID=8YFLogxK
U2 - 10.1080/02626667.2019.1602777
DO - 10.1080/02626667.2019.1602777
M3 - Article
AN - SCOPUS:85065156594
VL - 64
SP - 731
EP - 738
JO - Hydrological Sciences Journal
JF - Hydrological Sciences Journal
SN - 0262-6667
IS - 6
ER -