2012
DOI: 10.1002/qj.1998
|View full text |Cite
|
Sign up to set email alerts
|

Modelling climate impact on floods with ensemble climate projections

Abstract: The evidence provided by modelled assessments of future climate impact on flooding is fundamental to water resources and flood risk decision making. Impact models usually rely on climate projections from global and regional climate models (GCM/RCMs). However, challenges in representing precipitation events at catchment-scale resolution mean that decisions must be made on how to appropriately pre-process the meteorological variables from GCM/RCMs. Here the impacts on projected high flows of differing ensemble a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

2
113
0

Year Published

2012
2012
2016
2016

Publication Types

Select...
9

Relationship

1
8

Authors

Journals

citations
Cited by 109 publications
(115 citation statements)
references
References 53 publications
2
113
0
Order By: Relevance
“…The RCM/GCMs simulations are generally biased and cannot be used as forcing variables in drought projection without some form of prior bias correction. Several studies have been performed in which a bias correction method was applied to RCM data (e.g., [36][37][38][39]). In this study, the Quantile Mapping (QM) method [40][41][42] was applied for each catchment to correct the simulated precipitation and air temperature time series.…”
Section: Climate Model Projectionsmentioning
confidence: 99%
“…The RCM/GCMs simulations are generally biased and cannot be used as forcing variables in drought projection without some form of prior bias correction. Several studies have been performed in which a bias correction method was applied to RCM data (e.g., [36][37][38][39]). In this study, the Quantile Mapping (QM) method [40][41][42] was applied for each catchment to correct the simulated precipitation and air temperature time series.…”
Section: Climate Model Projectionsmentioning
confidence: 99%
“…Developing global-scale flood maps (Porter and Demeritt, 2012) is of increasing interest in the scientific community with great applicability in the (re)insurance industry. Global gridded precipitation data sets from satellites and reanalysis data sets derived from data assimilation systems are two main sources for deriving global flood hazard maps (Cloke et al, 2013;Kappes et al, 2012).…”
Section: Introductionmentioning
confidence: 99%
“…In smaller catchments, on the scale of a few RCM grid points and with quick response times, this can potentially lead to an underestimation of high flow situations, regardless of whether the RCM has a wet or dry mean bias over the catchment. Other reasons are smoothing between grid points and inadequate representation of the physical processes in the model code (Cloke et al, 2012). Therefore at present, impact studies that rely on a correct representation of precipitation, such as those dealing with flood risk, cannot usefully use direct RCM output.…”
Section: F Wetterhall Et Al: Conditioning Model Output Statistics Omentioning
confidence: 99%