TY - JOUR
T1 - Stationary vs. Non-Stationary Modelling of Flood Frequency Distribution across North-West England
AU - Hesarkazzazi, Sina
AU - Arabzadeh, Rezgar
AU - Hajibabaei, Mohsen
AU - Rauch, Wolfgang
AU - Kjeldsen, Thomas R.
AU - Prosdocimi, Ilaria
AU - Castellarin, Attilio
AU - Sitzenfrei, Robert
N1 - Funding Information:
The authors gratefully acknowledge the UK National River Flow Archive (NRFA) and National Weather Service (Met Office) for providing the river flow and climate data, respectively.
PY - 2021/12/31
Y1 - 2021/12/31
N2 - Extraordinary flood events occurred recently in northwest England, with several severe floods in Cumbria, Lancashire and the Manchester area in 2004, 2009 and 2015. These clustered extraordinary events have raised the question of whether any changes in the magnitude and frequency of river flows in the region can be detected. For this purpose, the annual maximum series of 39 river gauging stations in the study area are analysed. In particular, non-stationary models that include time, annual rainfall and annual temperature as predictors are investigated. Most records demonstrate a marked non-stationary behaviour and an increase of up to 75% in flood quantile estimates during the study period. Annual rainfall explains the largest proportion of variability in the peak flow series relative to other predictors considered in our study, providing practitioners with a useful framework for updating flood quantile estimates based on the dynamics of this highly accessible and informative climate indicator.
AB - Extraordinary flood events occurred recently in northwest England, with several severe floods in Cumbria, Lancashire and the Manchester area in 2004, 2009 and 2015. These clustered extraordinary events have raised the question of whether any changes in the magnitude and frequency of river flows in the region can be detected. For this purpose, the annual maximum series of 39 river gauging stations in the study area are analysed. In particular, non-stationary models that include time, annual rainfall and annual temperature as predictors are investigated. Most records demonstrate a marked non-stationary behaviour and an increase of up to 75% in flood quantile estimates during the study period. Annual rainfall explains the largest proportion of variability in the peak flow series relative to other predictors considered in our study, providing practitioners with a useful framework for updating flood quantile estimates based on the dynamics of this highly accessible and informative climate indicator.
KW - annual maxima (AM)
KW - Cumbria
KW - flood hazard assessment
KW - generalized logistic (GLO) model
KW - hydrological extremes
KW - non-stationary flood frequency analysis
KW - statistical hydrology
KW - UK
UR - http://www.scopus.com/inward/record.url?scp=85102925885&partnerID=8YFLogxK
U2 - 10.1080/02626667.2021.1884685
DO - 10.1080/02626667.2021.1884685
M3 - Article
AN - SCOPUS:85102925885
SN - 0262-6667
VL - 66
SP - 729
EP - 744
JO - Hydrological Sciences Journal
JF - Hydrological Sciences Journal
IS - 4
ER -
