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Regional frequency analysis of annual daily rainfall maxima in Skåne, Sweden
Stockholm University, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0003-2889-0263
2021 (English)In: International Journal of Climatology, ISSN 0899-8418, E-ISSN 1097-0088, Vol. 41, no 8, p. 4307-4320Article in journal (Refereed) Published
Abstract [en]

Extreme daily rainfall events are critical for the urban drainage system, human life, agriculture and small catchments. The information about extreme rainfall magnitudes and frequencies is immensely important for civil engineers, city planners, scientists related to water management, rescue operations and flood control works. This study illustrates the results of regional frequency analysis of annual maximum daily rainfall (AMDR) of Skåne County, Sweden. L‐moments based heterogeneity measure (H) reveals that the Skåne County is a homogeneous region. Based on the L‐moment ratio diagram and ZDist statistic results, the generalized normal (GNO) distribution is selected as the most suitable regional distribution. The accuracy measures used in K‐fold cross validation indicate that support vector machine (SVM) model is an appropriate model to find the index rainfall at ungauged sites in the region. The sites characteristics, elevation and latitude are identified as the most important variables to explain the variation in mean annual maximum daily rainfall (MAMDR). Finally, spatial maps of predicted MAMDR for different return periods are constructed by using index rainfall combined with regional quantiles. Spatial maps offer an overall view of the expected MAMDR in the region that is helpful for multiple decision makers including infrastructure planners, city planners, emergency managers, engineers and many others.

Place, publisher, year, edition, pages
2021. Vol. 41, no 8, p. 4307-4320
Keywords [en]
K-fold cross validation, L-moments, Monte Carlo simulation, quantile estimates, regional frequency analysis, spatial map
National Category
Probability Theory and Statistics Environmental Sciences
Identifiers
URN: urn:nbn:se:su:diva-191188DOI: 10.1002/joc.7074ISI: 000627551300001OAI: oai:DiVA.org:su-191188DiVA, id: diva2:1536595
Available from: 2021-03-11 Created: 2021-03-11 Last updated: 2022-02-25Bibliographically approved

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Ul Hassan, Mahmood

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CiteExportLink to record
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  • apa
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