000262017 001__ 262017
000262017 005__ 20190619220106.0
000262017 022__ $$a0022-1694
000262017 022__ $$a1879-2707
000262017 02470 $$a000447477200059$$2isi
000262017 0247_ $$a10.1016/j.jhydrol.2018.08.061$$2doi
000262017 037__ $$aARTICLE
000262017 245__ $$aDependence properties of spatial rainfall extremes and areal reduction factors
000262017 269__ $$a2018-10-01
000262017 260__ $$c2018-10-01
000262017 336__ $$aJournal Articles
000262017 520__ $$aAreal reduction factors (ARFs) transform an estimate of extreme rainfall at a point to an estimate of extreme rainfall over a spatial domain, and are commonly used in flood risk estimation. For applications such as the design of large infrastructure, dam safety and land use planning, ARFs are needed to estimate flood risk for very rare events that are often larger than the biggest historical events. The nature of the relationship between ARFs and frequency for long return periods is unclear as it depends on the asymptotic dependence structure of rainfall over a region, i.e., the extent to which rainfall from a surrounding region is extreme as rainfall at a point becomes more extreme. Miscalculating this for very rare events could lead to poor design of infrastructure. To investigate this, spatial rainfall processes are simulated using asymptotically dependent and independent models, and the implications for ARFs of the asymptotic assumptions are explored in a synthetic study. The models are then applied to a case study in Victoria, Australia, using 88 daily rainfall gauges with 50 years of data. The analysis shows that the observed data follow the behaviour of an asymptotically independent process, leading to ARFs that decrease with increasing return period. The study demonstrates that the use of inverted max-stable process models to simulate ARFs can provide a rigorous alternative to empirical approaches, particularly for long return periods requiring significant extrapolation from the data.
000262017 650__ $$aEngineering, Civil
000262017 650__ $$aGeosciences, Multidisciplinary
000262017 650__ $$aWater Resources
000262017 650__ $$aEngineering
000262017 650__ $$aGeology
000262017 650__ $$aWater Resources
000262017 6531_ $$aareal reduction factor
000262017 6531_ $$aasymptotic dependence
000262017 6531_ $$aasymptotic independence
000262017 6531_ $$aextreme rainfall
000262017 6531_ $$ainverted max-stable process
000262017 6531_ $$amax-stable process
000262017 6531_ $$amax-stable processes
000262017 6531_ $$abrown-resnick processes
000262017 6531_ $$apoint rainfall
000262017 6531_ $$ainference
000262017 6531_ $$atransformation
000262017 6531_ $$astatistics
000262017 6531_ $$asimulation
000262017 6531_ $$acurves
000262017 6531_ $$amodel
000262017 700__ $$aLe, Phuong Dong
000262017 700__ $$g111184$$aDavison, Anthony C.$$0240476
000262017 700__ $$g224237$$aEngelke, Sebastian$$0246548
000262017 700__ $$aLeonard, Michael
000262017 700__ $$aWestra, Seth
000262017 773__ $$q711-719$$j565$$tJournal Of Hydrology
000262017 8560_ $$fanthony.davison@epfl.ch
000262017 909C0 $$zJunod, Julien$$xU10124$$pSTAT$$manthony.davison@epfl.ch$$0252136
000262017 909CO $$particle$$ooai:infoscience.epfl.ch:262017$$pSB
000262017 961__ $$afantin.reichler@epfl.ch
000262017 973__ $$aEPFL$$sPUBLISHED$$rREVIEWED
000262017 981__ $$aoverwrite
000262017 980__ $$aARTICLE
000262017 999C0 $$zJunod, Julien$$xU10112$$pMATHAA$$mjulien.junod@epfl.ch$$0252437